#Set working directory to appropriate folder for inputs and outputs on Google Drive

#Initialize

Load data

load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/all_data_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Preprocess_GEX/second_timepoint_merged.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Filtering_cDNA/resistant_lineage_lists.RData')

load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/cis_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/cocl2_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/dabtram_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/dabtram_both_times_final_lineages.RData')

#Plot cell cycle

load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/all_data_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Preprocess_GEX/second_timepoint_merged.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Filtering_cDNA/resistant_lineage_lists.RData')

load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/cis_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/cocl2_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/dabtram_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/dabtram_both_times_final_lineages.RData')

Markers within each first drug object to find subgroups (ie analogous to NGFR/EGFR)

need to make Idents metadata object which says if the cells are included in the combined lins list, or if they were filtered

#load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/all_data_markers.RData')
cis_markers <- FindAllMarkers(cis, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
Calculating cluster 0

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~05s          
  |++                                                | 2 % ~04s          
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  |+++++++++++++++++++++++                           | 44% ~02s          
  |+++++++++++++++++++++++                           | 46% ~02s          
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  |++++++++++++++++++++++++++++++++                  | 63% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~01s          
  |++++++++++++++++++++++++++++++++++                | 67% ~01s          
  |++++++++++++++++++++++++++++++++++                | 68% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s  
Calculating cluster 1

  |                                                  | 0 % ~calculating  
  |++                                                | 3 % ~00s          
  |++++                                              | 7 % ~00s          
  |++++++                                            | 10% ~00s          
  |+++++++                                           | 14% ~00s          
  |+++++++++                                         | 17% ~00s          
  |+++++++++++                                       | 21% ~00s          
  |+++++++++++++                                     | 24% ~00s          
  |++++++++++++++                                    | 28% ~00s          
  |++++++++++++++++                                  | 31% ~00s          
  |++++++++++++++++++                                | 34% ~00s          
  |+++++++++++++++++++                               | 38% ~00s          
  |+++++++++++++++++++++                             | 41% ~00s          
  |+++++++++++++++++++++++                           | 45% ~00s          
  |+++++++++++++++++++++++++                         | 48% ~00s          
  |++++++++++++++++++++++++++                        | 52% ~00s          
  |++++++++++++++++++++++++++++                      | 55% ~00s          
  |++++++++++++++++++++++++++++++                    | 59% ~00s          
  |++++++++++++++++++++++++++++++++                  | 62% ~00s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~00s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~00s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~00s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~00s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s  
Calculating cluster 2

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~05s          
  |++                                                | 2 % ~04s          
  |++                                                | 4 % ~04s          
  |+++                                               | 5 % ~04s          
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  |++++                                              | 7 % ~04s          
  |+++++                                             | 8 % ~04s          
  |+++++                                             | 10% ~04s          
  |++++++                                            | 11% ~04s          
  |+++++++                                           | 12% ~04s          
  |+++++++                                           | 13% ~04s          
  |++++++++                                          | 14% ~04s          
  |++++++++                                          | 16% ~04s          
  |+++++++++                                         | 17% ~04s          
  |++++++++++                                        | 18% ~03s          
  |++++++++++                                        | 19% ~03s          
  |+++++++++++                                       | 20% ~03s          
  |+++++++++++                                       | 22% ~03s          
  |++++++++++++                                      | 23% ~03s          
  |+++++++++++++                                     | 24% ~03s          
  |+++++++++++++                                     | 25% ~03s          
  |++++++++++++++                                    | 27% ~03s          
  |++++++++++++++                                    | 28% ~06s          
  |+++++++++++++++                                   | 29% ~05s          
  |++++++++++++++++                                  | 30% ~05s          
  |++++++++++++++++                                  | 31% ~05s          
  |+++++++++++++++++                                 | 33% ~05s          
  |+++++++++++++++++                                 | 34% ~05s          
  |++++++++++++++++++                                | 35% ~05s          
  |+++++++++++++++++++                               | 36% ~04s          
  |+++++++++++++++++++                               | 37% ~04s          
  |++++++++++++++++++++                              | 39% ~04s          
  |++++++++++++++++++++                              | 40% ~04s          
  |+++++++++++++++++++++                             | 41% ~04s          
  |++++++++++++++++++++++                            | 42% ~04s          
  |++++++++++++++++++++++                            | 43% ~04s          
  |+++++++++++++++++++++++                           | 45% ~04s          
  |+++++++++++++++++++++++                           | 46% ~03s          
  |++++++++++++++++++++++++                          | 47% ~03s          
  |+++++++++++++++++++++++++                         | 48% ~03s          
  |+++++++++++++++++++++++++                         | 49% ~03s          
  |++++++++++++++++++++++++++                        | 51% ~03s          
  |++++++++++++++++++++++++++                        | 52% ~03s          
  |+++++++++++++++++++++++++++                       | 53% ~03s          
  |++++++++++++++++++++++++++++                      | 54% ~03s          
  |++++++++++++++++++++++++++++                      | 55% ~03s          
  |+++++++++++++++++++++++++++++                     | 57% ~03s          
  |+++++++++++++++++++++++++++++                     | 58% ~02s          
  |++++++++++++++++++++++++++++++                    | 59% ~02s          
  |+++++++++++++++++++++++++++++++                   | 60% ~02s          
  |+++++++++++++++++++++++++++++++                   | 61% ~02s          
  |++++++++++++++++++++++++++++++++                  | 63% ~02s          
  |++++++++++++++++++++++++++++++++                  | 64% ~02s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~02s          
  |++++++++++++++++++++++++++++++++++                | 66% ~02s          
  |++++++++++++++++++++++++++++++++++                | 67% ~02s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~02s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~02s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~02s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~02s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~02s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~02s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~02s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=06s  
Calculating cluster 3

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~02s          
  |++                                                | 3 % ~03s          
  |++                                                | 4 % ~02s          
  |+++                                               | 5 % ~02s          
  |++++                                              | 7 % ~02s          
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  |+++++                                             | 9 % ~02s          
  |++++++                                            | 11% ~02s          
  |++++++                                            | 12% ~02s          
  |+++++++                                           | 13% ~02s          
  |++++++++                                          | 14% ~02s          
  |++++++++                                          | 16% ~02s          
  |+++++++++                                         | 17% ~02s          
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  |+++++++++++++                                     | 25% ~02s          
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  |+++++++++++++++++                                 | 33% ~02s          
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  |+++++++++++++++++++                               | 37% ~02s          
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  |+++++++++++++++++++++                             | 41% ~02s          
  |++++++++++++++++++++++                            | 42% ~02s          
  |++++++++++++++++++++++                            | 43% ~02s          
  |+++++++++++++++++++++++                           | 45% ~02s          
  |++++++++++++++++++++++++                          | 46% ~01s          
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  |+++++++++++++++++++++++++                         | 49% ~01s          
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  |++++++++++++++++++++++++++++++++                  | 63% ~01s          
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  |+++++++++++++++++++++++++++++++++                 | 66% ~01s          
  |++++++++++++++++++++++++++++++++++                | 67% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s  
Calculating cluster 4

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~04s          
  |++                                                | 2 % ~04s          
  |++                                                | 3 % ~04s          
  |+++                                               | 5 % ~04s          
  |+++                                               | 6 % ~04s          
  |++++                                              | 7 % ~03s          
  |+++++                                             | 8 % ~03s          
  |+++++                                             | 9 % ~03s          
  |++++++                                            | 10% ~03s          
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  |++++++++++++++++++++++++++++++++++++              | 71% ~01s          
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  |++++++++++++++++++++++++++++++++++++++            | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s  
Calculating cluster 5

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~07s          
  |++                                                | 2 % ~06s          
  |++                                                | 3 % ~06s          
  |+++                                               | 5 % ~06s          
  |+++                                               | 6 % ~06s          
  |++++                                              | 7 % ~06s          
  |+++++                                             | 8 % ~06s          
  |+++++                                             | 9 % ~06s          
  |++++++                                            | 10% ~06s          
  |++++++                                            | 12% ~06s          
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  |++++++++                                          | 15% ~06s          
  |+++++++++                                         | 16% ~05s          
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  |++++++++++++++++                                  | 31% ~04s          
  |+++++++++++++++++                                 | 33% ~04s          
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  |+++++++++++++++++++++                             | 41% ~04s          
  |+++++++++++++++++++++                             | 42% ~04s          
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  |+++++++++++++++++++++++                           | 44% ~04s          
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  |++++++++++++++++++++++++                          | 47% ~04s          
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  |+++++++++++++++++++++++++                         | 50% ~03s          
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  |++++++++++++++++++++++++++++++++                  | 64% ~02s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~02s          
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  |+++++++++++++++++++++++++++++++++++               | 70% ~02s          
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  |+++++++++++++++++++++++++++++++++++++             | 73% ~02s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~02s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~02s          
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  |+++++++++++++++++++++++++++++++++++++++           | 78% ~01s          
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  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=07s  
Calculating cluster 6

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~03s          
  |++                                                | 2 % ~03s          
  |++                                                | 3 % ~03s          
  |+++                                               | 5 % ~03s          
  |+++                                               | 6 % ~03s          
  |++++                                              | 7 % ~03s          
  |++++                                              | 8 % ~02s          
  |+++++                                             | 9 % ~02s          
  |++++++                                            | 10% ~02s          
  |++++++                                            | 11% ~02s          
  |+++++++                                           | 12% ~02s          
  |+++++++                                           | 14% ~02s          
  |++++++++                                          | 15% ~02s          
  |++++++++                                          | 16% ~02s          
  |+++++++++                                         | 17% ~02s          
  |++++++++++                                        | 18% ~02s          
  |++++++++++                                        | 19% ~02s          
  |+++++++++++                                       | 20% ~02s          
  |+++++++++++                                       | 22% ~02s          
  |++++++++++++                                      | 23% ~02s          
  |++++++++++++                                      | 24% ~02s          
  |+++++++++++++                                     | 25% ~02s          
  |++++++++++++++                                    | 26% ~02s          
  |++++++++++++++                                    | 27% ~02s          
  |+++++++++++++++                                   | 28% ~02s          
  |+++++++++++++++                                   | 30% ~02s          
  |++++++++++++++++                                  | 31% ~02s          
  |++++++++++++++++                                  | 32% ~02s          
  |+++++++++++++++++                                 | 33% ~02s          
  |++++++++++++++++++                                | 34% ~02s          
  |++++++++++++++++++                                | 35% ~02s          
  |+++++++++++++++++++                               | 36% ~02s          
  |+++++++++++++++++++                               | 38% ~02s          
  |++++++++++++++++++++                              | 39% ~02s          
  |++++++++++++++++++++                              | 40% ~02s          
  |+++++++++++++++++++++                             | 41% ~02s          
  |++++++++++++++++++++++                            | 42% ~02s          
  |++++++++++++++++++++++                            | 43% ~02s          
  |+++++++++++++++++++++++                           | 44% ~01s          
  |+++++++++++++++++++++++                           | 45% ~01s          
  |++++++++++++++++++++++++                          | 47% ~01s          
  |++++++++++++++++++++++++                          | 48% ~01s          
  |+++++++++++++++++++++++++                         | 49% ~01s          
  |+++++++++++++++++++++++++                         | 50% ~01s          
  |++++++++++++++++++++++++++                        | 51% ~01s          
  |+++++++++++++++++++++++++++                       | 52% ~01s          
  |+++++++++++++++++++++++++++                       | 53% ~01s          
  |++++++++++++++++++++++++++++                      | 55% ~01s          
  |++++++++++++++++++++++++++++                      | 56% ~01s          
  |+++++++++++++++++++++++++++++                     | 57% ~01s          
  |+++++++++++++++++++++++++++++                     | 58% ~01s          
  |++++++++++++++++++++++++++++++                    | 59% ~01s          
  |+++++++++++++++++++++++++++++++                   | 60% ~01s          
  |+++++++++++++++++++++++++++++++                   | 61% ~01s          
  |++++++++++++++++++++++++++++++++                  | 62% ~01s          
  |++++++++++++++++++++++++++++++++                  | 64% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~01s          
  |++++++++++++++++++++++++++++++++++                | 67% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s  
Calculating cluster 7

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~02s          
  |++                                                | 2 % ~02s          
  |++                                                | 3 % ~02s          
  |+++                                               | 4 % ~02s          
  |+++                                               | 5 % ~02s          
  |++++                                              | 7 % ~02s          
  |++++                                              | 8 % ~02s          
  |+++++                                             | 9 % ~02s          
  |+++++                                             | 10% ~02s          
  |++++++                                            | 11% ~02s          
  |+++++++                                           | 12% ~02s          
  |+++++++                                           | 13% ~02s          
  |++++++++                                          | 14% ~02s          
  |++++++++                                          | 15% ~02s          
  |+++++++++                                         | 16% ~02s          
  |+++++++++                                         | 18% ~02s          
  |++++++++++                                        | 19% ~02s          
  |++++++++++                                        | 20% ~02s          
  |+++++++++++                                       | 21% ~02s          
  |+++++++++++                                       | 22% ~02s          
  |++++++++++++                                      | 23% ~02s          
  |+++++++++++++                                     | 24% ~02s          
  |+++++++++++++                                     | 25% ~02s          
  |++++++++++++++                                    | 26% ~02s          
  |++++++++++++++                                    | 27% ~02s          
  |+++++++++++++++                                   | 29% ~02s          
  |+++++++++++++++                                   | 30% ~02s          
  |++++++++++++++++                                  | 31% ~02s          
  |++++++++++++++++                                  | 32% ~02s          
  |+++++++++++++++++                                 | 33% ~02s          
  |++++++++++++++++++                                | 34% ~02s          
  |++++++++++++++++++                                | 35% ~02s          
  |+++++++++++++++++++                               | 36% ~02s          
  |+++++++++++++++++++                               | 37% ~02s          
  |++++++++++++++++++++                              | 38% ~02s          
  |++++++++++++++++++++                              | 40% ~02s          
  |+++++++++++++++++++++                             | 41% ~02s          
  |+++++++++++++++++++++                             | 42% ~02s          
  |++++++++++++++++++++++                            | 43% ~02s          
  |++++++++++++++++++++++                            | 44% ~02s          
  |+++++++++++++++++++++++                           | 45% ~02s          
  |++++++++++++++++++++++++                          | 46% ~01s          
  |++++++++++++++++++++++++                          | 47% ~01s          
  |+++++++++++++++++++++++++                         | 48% ~01s          
  |+++++++++++++++++++++++++                         | 49% ~01s          
  |++++++++++++++++++++++++++                        | 51% ~01s          
  |++++++++++++++++++++++++++                        | 52% ~01s          
  |+++++++++++++++++++++++++++                       | 53% ~01s          
  |+++++++++++++++++++++++++++                       | 54% ~01s          
  |++++++++++++++++++++++++++++                      | 55% ~01s          
  |+++++++++++++++++++++++++++++                     | 56% ~01s          
  |+++++++++++++++++++++++++++++                     | 57% ~01s          
  |++++++++++++++++++++++++++++++                    | 58% ~01s          
  |++++++++++++++++++++++++++++++                    | 59% ~01s          
  |+++++++++++++++++++++++++++++++                   | 60% ~01s          
  |+++++++++++++++++++++++++++++++                   | 62% ~01s          
  |++++++++++++++++++++++++++++++++                  | 63% ~01s          
  |++++++++++++++++++++++++++++++++                  | 64% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~01s          
  |++++++++++++++++++++++++++++++++++                | 67% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s  
Calculating cluster 8

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~05s          
  |++                                                | 2 % ~05s          
  |++                                                | 3 % ~05s          
  |+++                                               | 4 % ~05s          
  |+++                                               | 5 % ~05s          
  |++++                                              | 6 % ~05s          
  |++++                                              | 7 % ~05s          
  |+++++                                             | 8 % ~05s          
  |+++++                                             | 9 % ~05s          
  |++++++                                            | 11% ~05s          
  |++++++                                            | 12% ~04s          
  |+++++++                                           | 13% ~04s          
  |+++++++                                           | 14% ~04s          
  |++++++++                                          | 15% ~04s          
  |++++++++                                          | 16% ~04s          
  |+++++++++                                         | 17% ~04s          
  |+++++++++                                         | 18% ~04s          
  |++++++++++                                        | 19% ~04s          
  |++++++++++                                        | 20% ~04s          
  |+++++++++++                                       | 21% ~04s          
  |++++++++++++                                      | 22% ~04s          
  |++++++++++++                                      | 23% ~04s          
  |+++++++++++++                                     | 24% ~04s          
  |+++++++++++++                                     | 25% ~04s          
  |++++++++++++++                                    | 26% ~04s          
  |++++++++++++++                                    | 27% ~04s          
  |+++++++++++++++                                   | 28% ~04s          
  |+++++++++++++++                                   | 29% ~04s          
  |++++++++++++++++                                  | 31% ~03s          
  |++++++++++++++++                                  | 32% ~03s          
  |+++++++++++++++++                                 | 33% ~03s          
  |+++++++++++++++++                                 | 34% ~03s          
  |++++++++++++++++++                                | 35% ~03s          
  |++++++++++++++++++                                | 36% ~03s          
  |+++++++++++++++++++                               | 37% ~03s          
  |+++++++++++++++++++                               | 38% ~03s          
  |++++++++++++++++++++                              | 39% ~03s          
  |++++++++++++++++++++                              | 40% ~03s          
  |+++++++++++++++++++++                             | 41% ~03s          
  |++++++++++++++++++++++                            | 42% ~03s          
  |++++++++++++++++++++++                            | 43% ~03s          
  |+++++++++++++++++++++++                           | 44% ~03s          
  |+++++++++++++++++++++++                           | 45% ~03s          
  |++++++++++++++++++++++++                          | 46% ~03s          
  |++++++++++++++++++++++++                          | 47% ~03s          
  |+++++++++++++++++++++++++                         | 48% ~03s          
  |+++++++++++++++++++++++++                         | 49% ~03s          
  |++++++++++++++++++++++++++                        | 51% ~02s          
  |++++++++++++++++++++++++++                        | 52% ~02s          
  |+++++++++++++++++++++++++++                       | 53% ~02s          
  |+++++++++++++++++++++++++++                       | 54% ~02s          
  |++++++++++++++++++++++++++++                      | 55% ~02s          
  |++++++++++++++++++++++++++++                      | 56% ~02s          
  |+++++++++++++++++++++++++++++                     | 57% ~02s          
  |+++++++++++++++++++++++++++++                     | 58% ~02s          
  |++++++++++++++++++++++++++++++                    | 59% ~02s          
  |++++++++++++++++++++++++++++++                    | 60% ~02s          
  |+++++++++++++++++++++++++++++++                   | 61% ~02s          
  |++++++++++++++++++++++++++++++++                  | 62% ~02s          
  |++++++++++++++++++++++++++++++++                  | 63% ~02s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~02s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~02s          
  |++++++++++++++++++++++++++++++++++                | 66% ~02s          
  |++++++++++++++++++++++++++++++++++                | 67% ~02s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~02s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~02s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s  
cocl2_markers <- FindAllMarkers(cocl2, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
Calculating cluster 0

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~03s          
  |++                                                | 2 % ~04s          
  |++                                                | 3 % ~04s          
  |+++                                               | 4 % ~04s          
  |+++                                               | 5 % ~04s          
  |++++                                              | 6 % ~03s          
  |++++                                              | 7 % ~03s          
  |+++++                                             | 9 % ~03s          
  |+++++                                             | 10% ~03s          
  |++++++                                            | 11% ~03s          
  |++++++                                            | 12% ~03s          
  |+++++++                                           | 13% ~03s          
  |+++++++                                           | 14% ~03s          
  |++++++++                                          | 15% ~03s          
  |++++++++                                          | 16% ~03s          
  |+++++++++                                         | 17% ~03s          
  |++++++++++                                        | 18% ~03s          
  |++++++++++                                        | 19% ~03s          
  |+++++++++++                                       | 20% ~03s          
  |+++++++++++                                       | 21% ~03s          
  |++++++++++++                                      | 22% ~03s          
  |++++++++++++                                      | 23% ~03s          
  |+++++++++++++                                     | 24% ~03s          
  |+++++++++++++                                     | 26% ~03s          
  |++++++++++++++                                    | 27% ~03s          
  |++++++++++++++                                    | 28% ~03s          
  |+++++++++++++++                                   | 29% ~03s          
  |+++++++++++++++                                   | 30% ~03s          
  |++++++++++++++++                                  | 31% ~03s          
  |++++++++++++++++                                  | 32% ~03s          
  |+++++++++++++++++                                 | 33% ~03s          
  |++++++++++++++++++                                | 34% ~03s          
  |++++++++++++++++++                                | 35% ~02s          
  |+++++++++++++++++++                               | 36% ~02s          
  |+++++++++++++++++++                               | 37% ~02s          
  |++++++++++++++++++++                              | 38% ~02s          
  |++++++++++++++++++++                              | 39% ~02s          
  |+++++++++++++++++++++                             | 40% ~02s          
  |+++++++++++++++++++++                             | 41% ~02s          
  |++++++++++++++++++++++                            | 43% ~02s          
  |++++++++++++++++++++++                            | 44% ~02s          
  |+++++++++++++++++++++++                           | 45% ~02s          
  |+++++++++++++++++++++++                           | 46% ~02s          
  |++++++++++++++++++++++++                          | 47% ~02s          
  |++++++++++++++++++++++++                          | 48% ~02s          
  |+++++++++++++++++++++++++                         | 49% ~02s          
  |+++++++++++++++++++++++++                         | 50% ~02s          
  |++++++++++++++++++++++++++                        | 51% ~02s          
  |+++++++++++++++++++++++++++                       | 52% ~02s          
  |+++++++++++++++++++++++++++                       | 53% ~02s          
  |++++++++++++++++++++++++++++                      | 54% ~02s          
  |++++++++++++++++++++++++++++                      | 55% ~02s          
  |+++++++++++++++++++++++++++++                     | 56% ~02s          
  |+++++++++++++++++++++++++++++                     | 57% ~02s          
  |++++++++++++++++++++++++++++++                    | 59% ~02s          
  |++++++++++++++++++++++++++++++                    | 60% ~02s          
  |+++++++++++++++++++++++++++++++                   | 61% ~01s          
  |+++++++++++++++++++++++++++++++                   | 62% ~01s          
  |++++++++++++++++++++++++++++++++                  | 63% ~01s          
  |++++++++++++++++++++++++++++++++                  | 64% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~01s          
  |++++++++++++++++++++++++++++++++++                | 67% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s  
Calculating cluster 1

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~01s          
  |++                                                | 2 % ~01s          
  |++                                                | 4 % ~01s          
  |+++                                               | 5 % ~01s          
  |++++                                              | 6 % ~01s          
  |++++                                              | 8 % ~01s          
  |+++++                                             | 9 % ~01s          
  |+++++                                             | 10% ~01s          
  |++++++                                            | 11% ~01s          
  |+++++++                                           | 12% ~01s          
  |+++++++                                           | 14% ~01s          
  |++++++++                                          | 15% ~01s          
  |+++++++++                                         | 16% ~01s          
  |+++++++++                                         | 18% ~01s          
  |++++++++++                                        | 19% ~01s          
  |++++++++++                                        | 20% ~01s          
  |+++++++++++                                       | 21% ~01s          
  |++++++++++++                                      | 22% ~01s          
  |++++++++++++                                      | 24% ~01s          
  |+++++++++++++                                     | 25% ~01s          
  |++++++++++++++                                    | 26% ~01s          
  |++++++++++++++                                    | 28% ~01s          
  |+++++++++++++++                                   | 29% ~01s          
  |+++++++++++++++                                   | 30% ~01s          
  |++++++++++++++++                                  | 31% ~01s          
  |+++++++++++++++++                                 | 32% ~01s          
  |+++++++++++++++++                                 | 34% ~01s          
  |++++++++++++++++++                                | 35% ~01s          
  |+++++++++++++++++++                               | 36% ~01s          
  |+++++++++++++++++++                               | 38% ~01s          
  |++++++++++++++++++++                              | 39% ~01s          
  |++++++++++++++++++++                              | 40% ~01s          
  |+++++++++++++++++++++                             | 41% ~01s          
  |++++++++++++++++++++++                            | 42% ~01s          
  |++++++++++++++++++++++                            | 44% ~01s          
  |+++++++++++++++++++++++                           | 45% ~01s          
  |++++++++++++++++++++++++                          | 46% ~01s          
  |++++++++++++++++++++++++                          | 48% ~01s          
  |+++++++++++++++++++++++++                         | 49% ~01s          
  |+++++++++++++++++++++++++                         | 50% ~01s          
  |++++++++++++++++++++++++++                        | 51% ~01s          
  |+++++++++++++++++++++++++++                       | 52% ~01s          
  |+++++++++++++++++++++++++++                       | 54% ~01s          
  |++++++++++++++++++++++++++++                      | 55% ~01s          
  |+++++++++++++++++++++++++++++                     | 56% ~01s          
  |+++++++++++++++++++++++++++++                     | 58% ~01s          
  |++++++++++++++++++++++++++++++                    | 59% ~00s          
  |++++++++++++++++++++++++++++++                    | 60% ~00s          
  |+++++++++++++++++++++++++++++++                   | 61% ~00s          
  |++++++++++++++++++++++++++++++++                  | 62% ~00s          
  |++++++++++++++++++++++++++++++++                  | 64% ~00s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~00s          
  |++++++++++++++++++++++++++++++++++                | 66% ~00s          
  |++++++++++++++++++++++++++++++++++                | 68% ~00s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~00s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~00s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~00s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~00s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~00s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~00s          
  |+++++++++++++++++++++++++++++++++++++++           | 76% ~00s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~00s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~00s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s  
Calculating cluster 2

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~02s          
  |++                                                | 3 % ~01m 03s      
  |++                                                | 4 % ~42s          
  |+++                                               | 5 % ~32s          
  |++++                                              | 7 % ~25s          
  |++++                                              | 8 % ~21s          
  |+++++                                             | 9 % ~18s          
  |++++++                                            | 11% ~16s          
  |++++++                                            | 12% ~14s          
  |+++++++                                           | 13% ~13s          
  |++++++++                                          | 14% ~11s          
  |++++++++                                          | 16% ~10s          
  |+++++++++                                         | 17% ~10s          
  |++++++++++                                        | 18% ~09s          
  |++++++++++                                        | 20% ~08s          
  |+++++++++++                                       | 21% ~08s          
  |++++++++++++                                      | 22% ~07s          
  |++++++++++++                                      | 24% ~07s          
  |+++++++++++++                                     | 25% ~06s          
  |++++++++++++++                                    | 26% ~06s          
  |++++++++++++++                                    | 28% ~06s          
  |+++++++++++++++                                   | 29% ~05s          
  |++++++++++++++++                                  | 30% ~05s          
  |++++++++++++++++                                  | 32% ~05s          
  |+++++++++++++++++                                 | 33% ~05s          
  |++++++++++++++++++                                | 34% ~04s          
  |++++++++++++++++++                                | 36% ~04s          
  |+++++++++++++++++++                               | 37% ~04s          
  |++++++++++++++++++++                              | 38% ~04s          
  |++++++++++++++++++++                              | 39% ~04s          
  |+++++++++++++++++++++                             | 41% ~03s          
  |++++++++++++++++++++++                            | 42% ~03s          
  |++++++++++++++++++++++                            | 43% ~03s          
  |+++++++++++++++++++++++                           | 45% ~03s          
  |++++++++++++++++++++++++                          | 46% ~03s          
  |++++++++++++++++++++++++                          | 47% ~03s          
  |+++++++++++++++++++++++++                         | 49% ~03s          
  |+++++++++++++++++++++++++                         | 50% ~03s          
  |++++++++++++++++++++++++++                        | 51% ~02s          
  |+++++++++++++++++++++++++++                       | 53% ~02s          
  |+++++++++++++++++++++++++++                       | 54% ~02s          
  |++++++++++++++++++++++++++++                      | 55% ~02s          
  |+++++++++++++++++++++++++++++                     | 57% ~02s          
  |+++++++++++++++++++++++++++++                     | 58% ~02s          
  |++++++++++++++++++++++++++++++                    | 59% ~02s          
  |+++++++++++++++++++++++++++++++                   | 61% ~02s          
  |+++++++++++++++++++++++++++++++                   | 62% ~02s          
  |++++++++++++++++++++++++++++++++                  | 63% ~02s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~02s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~01s          
  |++++++++++++++++++++++++++++++++++                | 67% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s  
Calculating cluster 3

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~04s          
  |++                                                | 2 % ~04s          
  |++                                                | 3 % ~04s          
  |+++                                               | 4 % ~04s          
  |+++                                               | 5 % ~04s          
  |++++                                              | 7 % ~04s          
  |++++                                              | 8 % ~04s          
  |+++++                                             | 9 % ~04s          
  |+++++                                             | 10% ~04s          
  |++++++                                            | 11% ~03s          
  |+++++++                                           | 12% ~03s          
  |+++++++                                           | 13% ~03s          
  |++++++++                                          | 14% ~03s          
  |++++++++                                          | 15% ~03s          
  |+++++++++                                         | 16% ~03s          
  |+++++++++                                         | 18% ~03s          
  |++++++++++                                        | 19% ~03s          
  |++++++++++                                        | 20% ~03s          
  |+++++++++++                                       | 21% ~03s          
  |+++++++++++                                       | 22% ~03s          
  |++++++++++++                                      | 23% ~03s          
  |+++++++++++++                                     | 24% ~03s          
  |+++++++++++++                                     | 25% ~03s          
  |++++++++++++++                                    | 26% ~03s          
  |++++++++++++++                                    | 27% ~03s          
  |+++++++++++++++                                   | 29% ~03s          
  |+++++++++++++++                                   | 30% ~03s          
  |++++++++++++++++                                  | 31% ~03s          
  |++++++++++++++++                                  | 32% ~03s          
  |+++++++++++++++++                                 | 33% ~03s          
  |++++++++++++++++++                                | 34% ~03s          
  |++++++++++++++++++                                | 35% ~03s          
  |+++++++++++++++++++                               | 36% ~02s          
  |+++++++++++++++++++                               | 37% ~02s          
  |++++++++++++++++++++                              | 38% ~02s          
  |++++++++++++++++++++                              | 40% ~02s          
  |+++++++++++++++++++++                             | 41% ~02s          
  |+++++++++++++++++++++                             | 42% ~02s          
  |++++++++++++++++++++++                            | 43% ~02s          
  |++++++++++++++++++++++                            | 44% ~02s          
  |+++++++++++++++++++++++                           | 45% ~02s          
  |++++++++++++++++++++++++                          | 46% ~02s          
  |++++++++++++++++++++++++                          | 47% ~02s          
  |+++++++++++++++++++++++++                         | 48% ~02s          
  |+++++++++++++++++++++++++                         | 49% ~02s          
  |++++++++++++++++++++++++++                        | 51% ~02s          
  |++++++++++++++++++++++++++                        | 52% ~02s          
  |+++++++++++++++++++++++++++                       | 53% ~02s          
  |+++++++++++++++++++++++++++                       | 54% ~02s          
  |++++++++++++++++++++++++++++                      | 55% ~02s          
  |+++++++++++++++++++++++++++++                     | 56% ~02s          
  |+++++++++++++++++++++++++++++                     | 57% ~02s          
  |++++++++++++++++++++++++++++++                    | 58% ~02s          
  |++++++++++++++++++++++++++++++                    | 59% ~02s          
  |+++++++++++++++++++++++++++++++                   | 60% ~02s          
  |+++++++++++++++++++++++++++++++                   | 62% ~01s          
  |++++++++++++++++++++++++++++++++                  | 63% ~01s          
  |++++++++++++++++++++++++++++++++                  | 64% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~01s          
  |++++++++++++++++++++++++++++++++++                | 67% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s  
Calculating cluster 4

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~02m 19s      
  |+                                                 | 2 % ~01m 10s      
  |++                                                | 3 % ~47s          
  |++                                                | 4 % ~36s          
  |+++                                               | 5 % ~29s          
  |+++                                               | 6 % ~24s          
  |++++                                              | 7 % ~21s          
  |++++                                              | 8 % ~18s          
  |+++++                                             | 9 % ~16s          
  |+++++                                             | 10% ~15s          
  |++++++                                            | 11% ~13s          
  |++++++                                            | 12% ~12s          
  |+++++++                                           | 13% ~11s          
  |+++++++                                           | 14% ~11s          
  |++++++++                                          | 15% ~10s          
  |++++++++                                          | 16% ~09s          
  |+++++++++                                         | 17% ~09s          
  |+++++++++                                         | 18% ~08s          
  |++++++++++                                        | 19% ~08s          
  |++++++++++                                        | 20% ~08s          
  |+++++++++++                                       | 21% ~07s          
  |+++++++++++                                       | 22% ~07s          
  |++++++++++++                                      | 23% ~07s          
  |++++++++++++                                      | 24% ~06s          
  |+++++++++++++                                     | 25% ~06s          
  |+++++++++++++                                     | 26% ~06s          
  |++++++++++++++                                    | 27% ~06s          
  |++++++++++++++                                    | 28% ~05s          
  |+++++++++++++++                                   | 29% ~05s          
  |+++++++++++++++                                   | 30% ~05s          
  |++++++++++++++++                                  | 31% ~05s          
  |++++++++++++++++                                  | 32% ~05s          
  |+++++++++++++++++                                 | 33% ~05s          
  |+++++++++++++++++                                 | 34% ~04s          
  |++++++++++++++++++                                | 35% ~04s          
  |++++++++++++++++++                                | 36% ~04s          
  |+++++++++++++++++++                               | 37% ~04s          
  |+++++++++++++++++++                               | 38% ~04s          
  |++++++++++++++++++++                              | 39% ~04s          
  |++++++++++++++++++++                              | 40% ~04s          
  |+++++++++++++++++++++                             | 41% ~04s          
  |+++++++++++++++++++++                             | 42% ~03s          
  |++++++++++++++++++++++                            | 43% ~03s          
  |++++++++++++++++++++++                            | 44% ~03s          
  |+++++++++++++++++++++++                           | 45% ~03s          
  |+++++++++++++++++++++++                           | 46% ~03s          
  |++++++++++++++++++++++++                          | 47% ~03s          
  |++++++++++++++++++++++++                          | 48% ~03s          
  |+++++++++++++++++++++++++                         | 49% ~03s          
  |+++++++++++++++++++++++++                         | 50% ~03s          
  |++++++++++++++++++++++++++                        | 51% ~03s          
  |++++++++++++++++++++++++++                        | 52% ~03s          
  |+++++++++++++++++++++++++++                       | 53% ~02s          
  |+++++++++++++++++++++++++++                       | 54% ~02s          
  |++++++++++++++++++++++++++++                      | 55% ~02s          
  |++++++++++++++++++++++++++++                      | 56% ~02s          
  |+++++++++++++++++++++++++++++                     | 57% ~02s          
  |+++++++++++++++++++++++++++++                     | 58% ~02s          
  |++++++++++++++++++++++++++++++                    | 59% ~02s          
  |++++++++++++++++++++++++++++++                    | 60% ~02s          
  |+++++++++++++++++++++++++++++++                   | 61% ~02s          
  |+++++++++++++++++++++++++++++++                   | 62% ~02s          
  |++++++++++++++++++++++++++++++++                  | 63% ~02s          
  |++++++++++++++++++++++++++++++++                  | 64% ~02s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~02s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~02s          
  |++++++++++++++++++++++++++++++++++                | 67% ~02s          
  |++++++++++++++++++++++++++++++++++                | 68% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s  
Calculating cluster 5

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~13s          
  |++                                                | 3 % ~08s          
  |++                                                | 4 % ~06s          
  |+++                                               | 5 % ~05s          
  |++++                                              | 6 % ~04s          
  |++++                                              | 8 % ~04s          
  |+++++                                             | 9 % ~04s          
  |++++++                                            | 10% ~03s          
  |++++++                                            | 11% ~03s          
  |+++++++                                           | 13% ~03s          
  |+++++++                                           | 14% ~03s          
  |++++++++                                          | 15% ~03s          
  |+++++++++                                         | 16% ~03s          
  |+++++++++                                         | 18% ~03s          
  |++++++++++                                        | 19% ~03s          
  |+++++++++++                                       | 20% ~03s          
  |+++++++++++                                       | 22% ~03s          
  |++++++++++++                                      | 23% ~02s          
  |+++++++++++++                                     | 24% ~02s          
  |+++++++++++++                                     | 25% ~02s          
  |++++++++++++++                                    | 27% ~02s          
  |++++++++++++++                                    | 28% ~02s          
  |+++++++++++++++                                   | 29% ~02s          
  |++++++++++++++++                                  | 30% ~02s          
  |++++++++++++++++                                  | 32% ~02s          
  |+++++++++++++++++                                 | 33% ~02s          
  |++++++++++++++++++                                | 34% ~02s          
  |++++++++++++++++++                                | 35% ~02s          
  |+++++++++++++++++++                               | 37% ~02s          
  |+++++++++++++++++++                               | 38% ~02s          
  |++++++++++++++++++++                              | 39% ~02s          
  |+++++++++++++++++++++                             | 41% ~02s          
  |+++++++++++++++++++++                             | 42% ~02s          
  |++++++++++++++++++++++                            | 43% ~02s          
  |+++++++++++++++++++++++                           | 44% ~02s          
  |+++++++++++++++++++++++                           | 46% ~02s          
  |++++++++++++++++++++++++                          | 47% ~02s          
  |+++++++++++++++++++++++++                         | 48% ~01s          
  |+++++++++++++++++++++++++                         | 49% ~01s          
  |++++++++++++++++++++++++++                        | 51% ~01s          
  |++++++++++++++++++++++++++                        | 52% ~01s          
  |+++++++++++++++++++++++++++                       | 53% ~01s          
  |++++++++++++++++++++++++++++                      | 54% ~01s          
  |++++++++++++++++++++++++++++                      | 56% ~01s          
  |+++++++++++++++++++++++++++++                     | 57% ~01s          
  |++++++++++++++++++++++++++++++                    | 58% ~01s          
  |++++++++++++++++++++++++++++++                    | 59% ~01s          
  |+++++++++++++++++++++++++++++++                   | 61% ~01s          
  |++++++++++++++++++++++++++++++++                  | 62% ~01s          
  |++++++++++++++++++++++++++++++++                  | 63% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~01s          
  |++++++++++++++++++++++++++++++++++                | 67% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s  
Calculating cluster 6

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~02s          
  |++                                                | 2 % ~02s          
  |++                                                | 3 % ~02s          
  |+++                                               | 4 % ~02s          
  |+++                                               | 5 % ~02s          
  |++++                                              | 6 % ~02s          
  |++++                                              | 7 % ~02s          
  |+++++                                             | 9 % ~02s          
  |+++++                                             | 10% ~02s          
  |++++++                                            | 11% ~02s          
  |++++++                                            | 12% ~02s          
  |+++++++                                           | 13% ~02s          
  |+++++++                                           | 14% ~02s          
  |++++++++                                          | 15% ~02s          
  |++++++++                                          | 16% ~02s          
  |+++++++++                                         | 17% ~02s          
  |++++++++++                                        | 18% ~02s          
  |++++++++++                                        | 19% ~02s          
  |+++++++++++                                       | 20% ~02s          
  |+++++++++++                                       | 21% ~02s          
  |++++++++++++                                      | 22% ~01s          
  |++++++++++++                                      | 23% ~01s          
  |+++++++++++++                                     | 24% ~01s          
  |+++++++++++++                                     | 26% ~01s          
  |++++++++++++++                                    | 27% ~01s          
  |++++++++++++++                                    | 28% ~01s          
  |+++++++++++++++                                   | 29% ~01s          
  |+++++++++++++++                                   | 30% ~01s          
  |++++++++++++++++                                  | 31% ~01s          
  |++++++++++++++++                                  | 32% ~01s          
  |+++++++++++++++++                                 | 33% ~01s          
  |++++++++++++++++++                                | 34% ~01s          
  |++++++++++++++++++                                | 35% ~01s          
  |+++++++++++++++++++                               | 36% ~01s          
  |+++++++++++++++++++                               | 37% ~01s          
  |++++++++++++++++++++                              | 38% ~01s          
  |++++++++++++++++++++                              | 39% ~01s          
  |+++++++++++++++++++++                             | 40% ~01s          
  |+++++++++++++++++++++                             | 41% ~01s          
  |++++++++++++++++++++++                            | 43% ~01s          
  |++++++++++++++++++++++                            | 44% ~01s          
  |+++++++++++++++++++++++                           | 45% ~01s          
  |+++++++++++++++++++++++                           | 46% ~01s          
  |++++++++++++++++++++++++                          | 47% ~01s          
  |++++++++++++++++++++++++                          | 48% ~01s          
  |+++++++++++++++++++++++++                         | 49% ~01s          
  |+++++++++++++++++++++++++                         | 50% ~01s          
  |++++++++++++++++++++++++++                        | 51% ~01s          
  |+++++++++++++++++++++++++++                       | 52% ~01s          
  |+++++++++++++++++++++++++++                       | 53% ~01s          
  |++++++++++++++++++++++++++++                      | 54% ~01s          
  |++++++++++++++++++++++++++++                      | 55% ~01s          
  |+++++++++++++++++++++++++++++                     | 56% ~01s          
  |+++++++++++++++++++++++++++++                     | 57% ~01s          
  |++++++++++++++++++++++++++++++                    | 59% ~01s          
  |++++++++++++++++++++++++++++++                    | 60% ~01s          
  |+++++++++++++++++++++++++++++++                   | 61% ~01s          
  |+++++++++++++++++++++++++++++++                   | 62% ~01s          
  |++++++++++++++++++++++++++++++++                  | 63% ~01s          
  |++++++++++++++++++++++++++++++++                  | 64% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~01s          
  |++++++++++++++++++++++++++++++++++                | 67% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~00s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~00s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~00s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~00s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~00s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s  
Calculating cluster 7

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~11s          
  |+                                                 | 2 % ~11s          
  |++                                                | 3 % ~11s          
  |++                                                | 4 % ~11s          
  |+++                                               | 5 % ~11s          
  |+++                                               | 6 % ~11s          
  |++++                                              | 7 % ~11s          
  |++++                                              | 8 % ~11s          
  |+++++                                             | 9 % ~11s          
  |+++++                                             | 10% ~11s          
  |++++++                                            | 11% ~10s          
  |++++++                                            | 12% ~10s          
  |+++++++                                           | 13% ~10s          
  |+++++++                                           | 14% ~10s          
  |++++++++                                          | 15% ~10s          
  |++++++++                                          | 16% ~10s          
  |+++++++++                                         | 17% ~10s          
  |+++++++++                                         | 18% ~10s          
  |++++++++++                                        | 19% ~10s          
  |++++++++++                                        | 20% ~09s          
  |+++++++++++                                       | 21% ~09s          
  |+++++++++++                                       | 22% ~09s          
  |++++++++++++                                      | 23% ~10s          
  |++++++++++++                                      | 24% ~10s          
  |+++++++++++++                                     | 25% ~10s          
  |+++++++++++++                                     | 26% ~09s          
  |++++++++++++++                                    | 27% ~09s          
  |++++++++++++++                                    | 28% ~09s          
  |+++++++++++++++                                   | 29% ~09s          
  |+++++++++++++++                                   | 30% ~09s          
  |++++++++++++++++                                  | 31% ~09s          
  |++++++++++++++++                                  | 32% ~09s          
  |+++++++++++++++++                                 | 33% ~08s          
  |+++++++++++++++++                                 | 34% ~08s          
  |++++++++++++++++++                                | 35% ~08s          
  |++++++++++++++++++                                | 36% ~08s          
  |+++++++++++++++++++                               | 37% ~08s          
  |+++++++++++++++++++                               | 38% ~08s          
  |++++++++++++++++++++                              | 39% ~08s          
  |++++++++++++++++++++                              | 40% ~07s          
  |+++++++++++++++++++++                             | 41% ~07s          
  |+++++++++++++++++++++                             | 42% ~07s          
  |++++++++++++++++++++++                            | 43% ~07s          
  |++++++++++++++++++++++                            | 44% ~07s          
  |+++++++++++++++++++++++                           | 45% ~07s          
  |+++++++++++++++++++++++                           | 46% ~07s          
  |++++++++++++++++++++++++                          | 47% ~06s          
  |++++++++++++++++++++++++                          | 48% ~06s          
  |+++++++++++++++++++++++++                         | 49% ~06s          
  |+++++++++++++++++++++++++                         | 50% ~06s          
  |++++++++++++++++++++++++++                        | 51% ~06s          
  |++++++++++++++++++++++++++                        | 52% ~06s          
  |+++++++++++++++++++++++++++                       | 53% ~06s          
  |+++++++++++++++++++++++++++                       | 54% ~06s          
  |++++++++++++++++++++++++++++                      | 55% ~05s          
  |++++++++++++++++++++++++++++                      | 56% ~05s          
  |+++++++++++++++++++++++++++++                     | 57% ~05s          
  |+++++++++++++++++++++++++++++                     | 58% ~05s          
  |++++++++++++++++++++++++++++++                    | 59% ~05s          
  |++++++++++++++++++++++++++++++                    | 60% ~05s          
  |+++++++++++++++++++++++++++++++                   | 61% ~05s          
  |+++++++++++++++++++++++++++++++                   | 62% ~05s          
  |++++++++++++++++++++++++++++++++                  | 63% ~04s          
  |++++++++++++++++++++++++++++++++                  | 64% ~04s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~04s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~04s          
  |++++++++++++++++++++++++++++++++++                | 67% ~04s          
  |++++++++++++++++++++++++++++++++++                | 68% ~04s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~04s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~04s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~03s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~03s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~03s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~03s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~03s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~03s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~03s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~03s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~03s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=12s  
Calculating cluster 8

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~04s          
  |++                                                | 2 % ~04s          
  |++                                                | 3 % ~04s          
  |+++                                               | 5 % ~04s          
  |+++                                               | 6 % ~04s          
  |++++                                              | 7 % ~04s          
  |+++++                                             | 8 % ~04s          
  |+++++                                             | 9 % ~04s          
  |++++++                                            | 10% ~04s          
  |++++++                                            | 11% ~03s          
  |+++++++                                           | 13% ~03s          
  |+++++++                                           | 14% ~03s          
  |++++++++                                          | 15% ~03s          
  |+++++++++                                         | 16% ~03s          
  |+++++++++                                         | 17% ~03s          
  |++++++++++                                        | 18% ~03s          
  |++++++++++                                        | 20% ~03s          
  |+++++++++++                                       | 21% ~03s          
  |+++++++++++                                       | 22% ~03s          
  |++++++++++++                                      | 23% ~03s          
  |+++++++++++++                                     | 24% ~03s          
  |+++++++++++++                                     | 25% ~03s          
  |++++++++++++++                                    | 26% ~03s          
  |++++++++++++++                                    | 28% ~03s          
  |+++++++++++++++                                   | 29% ~03s          
  |+++++++++++++++                                   | 30% ~03s          
  |++++++++++++++++                                  | 31% ~03s          
  |+++++++++++++++++                                 | 32% ~03s          
  |+++++++++++++++++                                 | 33% ~03s          
  |++++++++++++++++++                                | 34% ~02s          
  |++++++++++++++++++                                | 36% ~02s          
  |+++++++++++++++++++                               | 37% ~02s          
  |+++++++++++++++++++                               | 38% ~02s          
  |++++++++++++++++++++                              | 39% ~02s          
  |+++++++++++++++++++++                             | 40% ~02s          
  |+++++++++++++++++++++                             | 41% ~02s          
  |++++++++++++++++++++++                            | 43% ~02s          
  |++++++++++++++++++++++                            | 44% ~02s          
  |+++++++++++++++++++++++                           | 45% ~02s          
  |+++++++++++++++++++++++                           | 46% ~02s          
  |++++++++++++++++++++++++                          | 47% ~02s          
  |+++++++++++++++++++++++++                         | 48% ~02s          
  |+++++++++++++++++++++++++                         | 49% ~02s          
  |++++++++++++++++++++++++++                        | 51% ~02s          
  |++++++++++++++++++++++++++                        | 52% ~02s          
  |+++++++++++++++++++++++++++                       | 53% ~02s          
  |++++++++++++++++++++++++++++                      | 54% ~02s          
  |++++++++++++++++++++++++++++                      | 55% ~02s          
  |+++++++++++++++++++++++++++++                     | 56% ~02s          
  |+++++++++++++++++++++++++++++                     | 57% ~02s          
  |++++++++++++++++++++++++++++++                    | 59% ~02s          
  |++++++++++++++++++++++++++++++                    | 60% ~02s          
  |+++++++++++++++++++++++++++++++                   | 61% ~01s          
  |++++++++++++++++++++++++++++++++                  | 62% ~01s          
  |++++++++++++++++++++++++++++++++                  | 63% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~01s          
  |++++++++++++++++++++++++++++++++++                | 67% ~01s          
  |++++++++++++++++++++++++++++++++++                | 68% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s  
Calculating cluster 9

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~08s          
  |++                                                | 2 % ~08s          
  |++                                                | 3 % ~08s          
  |+++                                               | 4 % ~08s          
  |+++                                               | 5 % ~08s          
  |++++                                              | 6 % ~08s          
  |++++                                              | 7 % ~07s          
  |+++++                                             | 8 % ~07s          
  |+++++                                             | 9 % ~07s          
  |++++++                                            | 10% ~07s          
  |++++++                                            | 11% ~07s          
  |+++++++                                           | 12% ~07s          
  |+++++++                                           | 13% ~07s          
  |++++++++                                          | 14% ~07s          
  |++++++++                                          | 15% ~07s          
  |+++++++++                                         | 16% ~07s          
  |+++++++++                                         | 17% ~07s          
  |++++++++++                                        | 18% ~07s          
  |++++++++++                                        | 19% ~06s          
  |+++++++++++                                       | 20% ~06s          
  |+++++++++++                                       | 21% ~06s          
  |++++++++++++                                      | 22% ~06s          
  |++++++++++++                                      | 23% ~06s          
  |+++++++++++++                                     | 24% ~06s          
  |+++++++++++++                                     | 25% ~06s          
  |++++++++++++++                                    | 26% ~06s          
  |++++++++++++++                                    | 27% ~06s          
  |+++++++++++++++                                   | 28% ~06s          
  |+++++++++++++++                                   | 29% ~06s          
  |++++++++++++++++                                  | 30% ~06s          
  |++++++++++++++++                                  | 31% ~05s          
  |+++++++++++++++++                                 | 32% ~05s          
  |+++++++++++++++++                                 | 33% ~05s          
  |++++++++++++++++++                                | 34% ~05s          
  |++++++++++++++++++                                | 35% ~05s          
  |+++++++++++++++++++                               | 36% ~05s          
  |+++++++++++++++++++                               | 37% ~05s          
  |++++++++++++++++++++                              | 38% ~05s          
  |++++++++++++++++++++                              | 39% ~05s          
  |+++++++++++++++++++++                             | 40% ~05s          
  |+++++++++++++++++++++                             | 41% ~05s          
  |++++++++++++++++++++++                            | 42% ~05s          
  |++++++++++++++++++++++                            | 43% ~05s          
  |+++++++++++++++++++++++                           | 44% ~04s          
  |+++++++++++++++++++++++                           | 45% ~04s          
  |++++++++++++++++++++++++                          | 46% ~04s          
  |++++++++++++++++++++++++                          | 47% ~04s          
  |+++++++++++++++++++++++++                         | 48% ~04s          
  |+++++++++++++++++++++++++                         | 49% ~04s          
  |++++++++++++++++++++++++++                        | 51% ~04s          
  |++++++++++++++++++++++++++                        | 52% ~04s          
  |+++++++++++++++++++++++++++                       | 53% ~04s          
  |+++++++++++++++++++++++++++                       | 54% ~04s          
  |++++++++++++++++++++++++++++                      | 55% ~04s          
  |++++++++++++++++++++++++++++                      | 56% ~04s          
  |+++++++++++++++++++++++++++++                     | 57% ~03s          
  |+++++++++++++++++++++++++++++                     | 58% ~03s          
  |++++++++++++++++++++++++++++++                    | 59% ~03s          
  |++++++++++++++++++++++++++++++                    | 60% ~03s          
  |+++++++++++++++++++++++++++++++                   | 61% ~03s          
  |+++++++++++++++++++++++++++++++                   | 62% ~03s          
  |++++++++++++++++++++++++++++++++                  | 63% ~03s          
  |++++++++++++++++++++++++++++++++                  | 64% ~03s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~03s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~03s          
  |++++++++++++++++++++++++++++++++++                | 67% ~03s          
  |++++++++++++++++++++++++++++++++++                | 68% ~03s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~03s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~02s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~02s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~02s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~02s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~02s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~02s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~02s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~02s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s  
dabtram_markers <- FindAllMarkers(dabtram, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
Calculating cluster 0

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~04s          
  |++                                                | 2 % ~03s          
  |++                                                | 3 % ~03s          
  |+++                                               | 5 % ~03s          
  |+++                                               | 6 % ~03s          
  |++++                                              | 7 % ~03s          
  |++++                                              | 8 % ~03s          
  |+++++                                             | 9 % ~03s          
  |++++++                                            | 10% ~03s          
  |++++++                                            | 11% ~03s          
  |+++++++                                           | 12% ~03s          
  |+++++++                                           | 14% ~03s          
  |++++++++                                          | 15% ~03s          
  |++++++++                                          | 16% ~03s          
  |+++++++++                                         | 17% ~03s          
  |++++++++++                                        | 18% ~03s          
  |++++++++++                                        | 19% ~03s          
  |+++++++++++                                       | 20% ~03s          
  |+++++++++++                                       | 22% ~03s          
  |++++++++++++                                      | 23% ~03s          
  |++++++++++++                                      | 24% ~02s          
  |+++++++++++++                                     | 25% ~02s          
  |++++++++++++++                                    | 26% ~02s          
  |++++++++++++++                                    | 27% ~02s          
  |+++++++++++++++                                   | 28% ~02s          
  |+++++++++++++++                                   | 30% ~02s          
  |++++++++++++++++                                  | 31% ~02s          
  |++++++++++++++++                                  | 32% ~02s          
  |+++++++++++++++++                                 | 33% ~02s          
  |++++++++++++++++++                                | 34% ~02s          
  |++++++++++++++++++                                | 35% ~02s          
  |+++++++++++++++++++                               | 36% ~02s          
  |+++++++++++++++++++                               | 38% ~02s          
  |++++++++++++++++++++                              | 39% ~02s          
  |++++++++++++++++++++                              | 40% ~02s          
  |+++++++++++++++++++++                             | 41% ~02s          
  |++++++++++++++++++++++                            | 42% ~02s          
  |++++++++++++++++++++++                            | 43% ~02s          
  |+++++++++++++++++++++++                           | 44% ~02s          
  |+++++++++++++++++++++++                           | 45% ~02s          
  |++++++++++++++++++++++++                          | 47% ~02s          
  |++++++++++++++++++++++++                          | 48% ~02s          
  |+++++++++++++++++++++++++                         | 49% ~02s          
  |+++++++++++++++++++++++++                         | 50% ~02s          
  |++++++++++++++++++++++++++                        | 51% ~02s          
  |+++++++++++++++++++++++++++                       | 52% ~02s          
  |+++++++++++++++++++++++++++                       | 53% ~02s          
  |++++++++++++++++++++++++++++                      | 55% ~01s          
  |++++++++++++++++++++++++++++                      | 56% ~01s          
  |+++++++++++++++++++++++++++++                     | 57% ~01s          
  |+++++++++++++++++++++++++++++                     | 58% ~01s          
  |++++++++++++++++++++++++++++++                    | 59% ~01s          
  |+++++++++++++++++++++++++++++++                   | 60% ~01s          
  |+++++++++++++++++++++++++++++++                   | 61% ~01s          
  |++++++++++++++++++++++++++++++++                  | 62% ~01s          
  |++++++++++++++++++++++++++++++++                  | 64% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~01s          
  |++++++++++++++++++++++++++++++++++                | 67% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s  
Calculating cluster 1

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~04s          
  |++                                                | 2 % ~04s          
  |++                                                | 3 % ~04s          
  |+++                                               | 4 % ~04s          
  |+++                                               | 5 % ~03s          
  |++++                                              | 6 % ~03s          
  |++++                                              | 7 % ~03s          
  |+++++                                             | 8 % ~03s          
  |+++++                                             | 9 % ~03s          
  |++++++                                            | 10% ~03s          
  |++++++                                            | 11% ~03s          
  |+++++++                                           | 12% ~03s          
  |+++++++                                           | 13% ~03s          
  |++++++++                                          | 14% ~03s          
  |++++++++                                          | 15% ~03s          
  |+++++++++                                         | 16% ~03s          
  |+++++++++                                         | 17% ~03s          
  |++++++++++                                        | 18% ~03s          
  |++++++++++                                        | 19% ~03s          
  |+++++++++++                                       | 20% ~03s          
  |+++++++++++                                       | 21% ~03s          
  |++++++++++++                                      | 22% ~03s          
  |++++++++++++                                      | 23% ~03s          
  |+++++++++++++                                     | 24% ~03s          
  |+++++++++++++                                     | 26% ~03s          
  |++++++++++++++                                    | 27% ~03s          
  |++++++++++++++                                    | 28% ~03s          
  |+++++++++++++++                                   | 29% ~03s          
  |+++++++++++++++                                   | 30% ~02s          
  |++++++++++++++++                                  | 31% ~02s          
  |++++++++++++++++                                  | 32% ~02s          
  |+++++++++++++++++                                 | 33% ~02s          
  |+++++++++++++++++                                 | 34% ~02s          
  |++++++++++++++++++                                | 35% ~02s          
  |++++++++++++++++++                                | 36% ~02s          
  |+++++++++++++++++++                               | 37% ~02s          
  |+++++++++++++++++++                               | 38% ~02s          
  |++++++++++++++++++++                              | 39% ~02s          
  |++++++++++++++++++++                              | 40% ~02s          
  |+++++++++++++++++++++                             | 41% ~02s          
  |+++++++++++++++++++++                             | 42% ~02s          
  |++++++++++++++++++++++                            | 43% ~02s          
  |++++++++++++++++++++++                            | 44% ~02s          
  |+++++++++++++++++++++++                           | 45% ~02s          
  |+++++++++++++++++++++++                           | 46% ~02s          
  |++++++++++++++++++++++++                          | 47% ~02s          
  |++++++++++++++++++++++++                          | 48% ~02s          
  |+++++++++++++++++++++++++                         | 49% ~02s          
  |+++++++++++++++++++++++++                         | 50% ~02s          
  |++++++++++++++++++++++++++                        | 51% ~02s          
  |+++++++++++++++++++++++++++                       | 52% ~02s          
  |+++++++++++++++++++++++++++                       | 53% ~02s          
  |++++++++++++++++++++++++++++                      | 54% ~02s          
  |++++++++++++++++++++++++++++                      | 55% ~02s          
  |+++++++++++++++++++++++++++++                     | 56% ~02s          
  |+++++++++++++++++++++++++++++                     | 57% ~02s          
  |++++++++++++++++++++++++++++++                    | 58% ~01s          
  |++++++++++++++++++++++++++++++                    | 59% ~01s          
  |+++++++++++++++++++++++++++++++                   | 60% ~01s          
  |+++++++++++++++++++++++++++++++                   | 61% ~01s          
  |++++++++++++++++++++++++++++++++                  | 62% ~01s          
  |++++++++++++++++++++++++++++++++                  | 63% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~01s          
  |++++++++++++++++++++++++++++++++++                | 66% ~01s          
  |++++++++++++++++++++++++++++++++++                | 67% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s  
Calculating cluster 2

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~03s          
  |++                                                | 2 % ~04s          
  |++                                                | 3 % ~04s          
  |+++                                               | 5 % ~04s          
  |+++                                               | 6 % ~04s          
  |++++                                              | 7 % ~04s          
  |+++++                                             | 8 % ~03s          
  |+++++                                             | 9 % ~03s          
  |++++++                                            | 10% ~03s          
  |++++++                                            | 11% ~03s          
  |+++++++                                           | 13% ~03s          
  |+++++++                                           | 14% ~03s          
  |++++++++                                          | 15% ~03s          
  |+++++++++                                         | 16% ~03s          
  |+++++++++                                         | 17% ~03s          
  |++++++++++                                        | 18% ~03s          
  |++++++++++                                        | 20% ~03s          
  |+++++++++++                                       | 21% ~03s          
  |+++++++++++                                       | 22% ~03s          
  |++++++++++++                                      | 23% ~03s          
  |+++++++++++++                                     | 24% ~03s          
  |+++++++++++++                                     | 25% ~03s          
  |++++++++++++++                                    | 26% ~03s          
  |++++++++++++++                                    | 28% ~03s          
  |+++++++++++++++                                   | 29% ~03s          
  |+++++++++++++++                                   | 30% ~03s          
  |++++++++++++++++                                  | 31% ~03s          
  |+++++++++++++++++                                 | 32% ~02s          
  |+++++++++++++++++                                 | 33% ~02s          
  |++++++++++++++++++                                | 34% ~02s          
  |++++++++++++++++++                                | 36% ~02s          
  |+++++++++++++++++++                               | 37% ~02s          
  |+++++++++++++++++++                               | 38% ~02s          
  |++++++++++++++++++++                              | 39% ~02s          
  |+++++++++++++++++++++                             | 40% ~02s          
  |+++++++++++++++++++++                             | 41% ~02s          
  |++++++++++++++++++++++                            | 43% ~02s          
  |++++++++++++++++++++++                            | 44% ~02s          
  |+++++++++++++++++++++++                           | 45% ~02s          
  |+++++++++++++++++++++++                           | 46% ~02s          
  |++++++++++++++++++++++++                          | 47% ~02s          
  |+++++++++++++++++++++++++                         | 48% ~02s          
  |+++++++++++++++++++++++++                         | 49% ~02s          
  |++++++++++++++++++++++++++                        | 51% ~02s          
  |++++++++++++++++++++++++++                        | 52% ~02s          
  |+++++++++++++++++++++++++++                       | 53% ~02s          
  |++++++++++++++++++++++++++++                      | 54% ~02s          
  |++++++++++++++++++++++++++++                      | 55% ~02s          
  |+++++++++++++++++++++++++++++                     | 56% ~02s          
  |+++++++++++++++++++++++++++++                     | 57% ~02s          
  |++++++++++++++++++++++++++++++                    | 59% ~01s          
  |++++++++++++++++++++++++++++++                    | 60% ~01s          
  |+++++++++++++++++++++++++++++++                   | 61% ~01s          
  |++++++++++++++++++++++++++++++++                  | 62% ~01s          
  |++++++++++++++++++++++++++++++++                  | 63% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~01s          
  |++++++++++++++++++++++++++++++++++                | 67% ~01s          
  |++++++++++++++++++++++++++++++++++                | 68% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s  
Calculating cluster 3

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~03s          
  |++                                                | 2 % ~02s          
  |++                                                | 4 % ~02s          
  |+++                                               | 5 % ~02s          
  |++++                                              | 6 % ~02s          
  |++++                                              | 7 % ~02s          
  |+++++                                             | 9 % ~02s          
  |+++++                                             | 10% ~02s          
  |++++++                                            | 11% ~02s          
  |+++++++                                           | 12% ~02s          
  |+++++++                                           | 14% ~02s          
  |++++++++                                          | 15% ~02s          
  |+++++++++                                         | 16% ~02s          
  |+++++++++                                         | 17% ~02s          
  |++++++++++                                        | 19% ~02s          
  |++++++++++                                        | 20% ~02s          
  |+++++++++++                                       | 21% ~02s          
  |++++++++++++                                      | 22% ~02s          
  |++++++++++++                                      | 23% ~02s          
  |+++++++++++++                                     | 25% ~02s          
  |+++++++++++++                                     | 26% ~02s          
  |++++++++++++++                                    | 27% ~02s          
  |+++++++++++++++                                   | 28% ~02s          
  |+++++++++++++++                                   | 30% ~02s          
  |++++++++++++++++                                  | 31% ~02s          
  |+++++++++++++++++                                 | 32% ~02s          
  |+++++++++++++++++                                 | 33% ~02s          
  |++++++++++++++++++                                | 35% ~01s          
  |++++++++++++++++++                                | 36% ~01s          
  |+++++++++++++++++++                               | 37% ~01s          
  |++++++++++++++++++++                              | 38% ~01s          
  |++++++++++++++++++++                              | 40% ~01s          
  |+++++++++++++++++++++                             | 41% ~01s          
  |+++++++++++++++++++++                             | 42% ~01s          
  |++++++++++++++++++++++                            | 43% ~01s          
  |+++++++++++++++++++++++                           | 44% ~01s          
  |+++++++++++++++++++++++                           | 46% ~01s          
  |++++++++++++++++++++++++                          | 47% ~01s          
  |+++++++++++++++++++++++++                         | 48% ~01s          
  |+++++++++++++++++++++++++                         | 49% ~01s          
  |++++++++++++++++++++++++++                        | 51% ~01s          
  |++++++++++++++++++++++++++                        | 52% ~01s          
  |+++++++++++++++++++++++++++                       | 53% ~01s          
  |++++++++++++++++++++++++++++                      | 54% ~01s          
  |++++++++++++++++++++++++++++                      | 56% ~01s          
  |+++++++++++++++++++++++++++++                     | 57% ~01s          
  |++++++++++++++++++++++++++++++                    | 58% ~01s          
  |++++++++++++++++++++++++++++++                    | 59% ~01s          
  |+++++++++++++++++++++++++++++++                   | 60% ~01s          
  |+++++++++++++++++++++++++++++++                   | 62% ~01s          
  |++++++++++++++++++++++++++++++++                  | 63% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~01s          
  |++++++++++++++++++++++++++++++++++                | 67% ~01s          
  |++++++++++++++++++++++++++++++++++                | 68% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s  
Calculating cluster 4

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~08s          
  |++                                                | 2 % ~08s          
  |++                                                | 3 % ~08s          
  |+++                                               | 4 % ~07s          
  |+++                                               | 5 % ~07s          
  |++++                                              | 6 % ~07s          
  |++++                                              | 7 % ~07s          
  |+++++                                             | 8 % ~07s          
  |+++++                                             | 9 % ~07s          
  |++++++                                            | 10% ~07s          
  |++++++                                            | 11% ~07s          
  |+++++++                                           | 12% ~07s          
  |+++++++                                           | 13% ~07s          
  |++++++++                                          | 14% ~06s          
  |++++++++                                          | 15% ~06s          
  |+++++++++                                         | 16% ~06s          
  |+++++++++                                         | 17% ~06s          
  |++++++++++                                        | 18% ~06s          
  |++++++++++                                        | 19% ~06s          
  |+++++++++++                                       | 20% ~06s          
  |+++++++++++                                       | 21% ~06s          
  |++++++++++++                                      | 22% ~06s          
  |++++++++++++                                      | 23% ~06s          
  |+++++++++++++                                     | 24% ~06s          
  |+++++++++++++                                     | 25% ~06s          
  |++++++++++++++                                    | 26% ~06s          
  |++++++++++++++                                    | 27% ~05s          
  |+++++++++++++++                                   | 28% ~05s          
  |+++++++++++++++                                   | 29% ~05s          
  |++++++++++++++++                                  | 30% ~05s          
  |++++++++++++++++                                  | 31% ~05s          
  |+++++++++++++++++                                 | 32% ~05s          
  |+++++++++++++++++                                 | 33% ~05s          
  |++++++++++++++++++                                | 34% ~05s          
  |++++++++++++++++++                                | 35% ~05s          
  |+++++++++++++++++++                               | 36% ~05s          
  |+++++++++++++++++++                               | 37% ~05s          
  |++++++++++++++++++++                              | 38% ~05s          
  |++++++++++++++++++++                              | 39% ~05s          
  |+++++++++++++++++++++                             | 40% ~04s          
  |+++++++++++++++++++++                             | 41% ~04s          
  |++++++++++++++++++++++                            | 42% ~04s          
  |++++++++++++++++++++++                            | 43% ~04s          
  |+++++++++++++++++++++++                           | 44% ~04s          
  |+++++++++++++++++++++++                           | 45% ~04s          
  |++++++++++++++++++++++++                          | 46% ~04s          
  |++++++++++++++++++++++++                          | 47% ~04s          
  |+++++++++++++++++++++++++                         | 48% ~04s          
  |+++++++++++++++++++++++++                         | 49% ~04s          
  |++++++++++++++++++++++++++                        | 51% ~04s          
  |++++++++++++++++++++++++++                        | 52% ~04s          
  |+++++++++++++++++++++++++++                       | 53% ~04s          
  |+++++++++++++++++++++++++++                       | 54% ~03s          
  |++++++++++++++++++++++++++++                      | 55% ~03s          
  |++++++++++++++++++++++++++++                      | 56% ~03s          
  |+++++++++++++++++++++++++++++                     | 57% ~03s          
  |+++++++++++++++++++++++++++++                     | 58% ~03s          
  |++++++++++++++++++++++++++++++                    | 59% ~03s          
  |++++++++++++++++++++++++++++++                    | 60% ~03s          
  |+++++++++++++++++++++++++++++++                   | 61% ~03s          
  |+++++++++++++++++++++++++++++++                   | 62% ~03s          
  |++++++++++++++++++++++++++++++++                  | 63% ~03s          
  |++++++++++++++++++++++++++++++++                  | 64% ~03s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~03s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~03s          
  |++++++++++++++++++++++++++++++++++                | 67% ~03s          
  |++++++++++++++++++++++++++++++++++                | 68% ~02s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~02s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~02s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~02s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~02s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~02s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~02s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~02s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~02s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~02s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s  
Calculating cluster 5

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~06s          
  |++                                                | 2 % ~06s          
  |++                                                | 3 % ~06s          
  |+++                                               | 4 % ~06s          
  |+++                                               | 5 % ~06s          
  |++++                                              | 6 % ~06s          
  |++++                                              | 7 % ~06s          
  |+++++                                             | 8 % ~06s          
  |+++++                                             | 9 % ~05s          
  |++++++                                            | 10% ~05s          
  |++++++                                            | 11% ~05s          
  |+++++++                                           | 12% ~05s          
  |+++++++                                           | 13% ~05s          
  |++++++++                                          | 14% ~05s          
  |++++++++                                          | 15% ~05s          
  |+++++++++                                         | 16% ~05s          
  |+++++++++                                         | 17% ~05s          
  |++++++++++                                        | 18% ~05s          
  |++++++++++                                        | 19% ~05s          
  |+++++++++++                                       | 20% ~05s          
  |+++++++++++                                       | 21% ~05s          
  |++++++++++++                                      | 22% ~05s          
  |++++++++++++                                      | 23% ~05s          
  |+++++++++++++                                     | 24% ~05s          
  |+++++++++++++                                     | 26% ~04s          
  |++++++++++++++                                    | 27% ~04s          
  |++++++++++++++                                    | 28% ~04s          
  |+++++++++++++++                                   | 29% ~04s          
  |+++++++++++++++                                   | 30% ~04s          
  |++++++++++++++++                                  | 31% ~04s          
  |++++++++++++++++                                  | 32% ~04s          
  |+++++++++++++++++                                 | 33% ~04s          
  |+++++++++++++++++                                 | 34% ~04s          
  |++++++++++++++++++                                | 35% ~04s          
  |++++++++++++++++++                                | 36% ~04s          
  |+++++++++++++++++++                               | 37% ~04s          
  |+++++++++++++++++++                               | 38% ~04s          
  |++++++++++++++++++++                              | 39% ~04s          
  |++++++++++++++++++++                              | 40% ~04s          
  |+++++++++++++++++++++                             | 41% ~04s          
  |+++++++++++++++++++++                             | 42% ~03s          
  |++++++++++++++++++++++                            | 43% ~03s          
  |++++++++++++++++++++++                            | 44% ~03s          
  |+++++++++++++++++++++++                           | 45% ~03s          
  |+++++++++++++++++++++++                           | 46% ~03s          
  |++++++++++++++++++++++++                          | 47% ~03s          
  |++++++++++++++++++++++++                          | 48% ~03s          
  |+++++++++++++++++++++++++                         | 49% ~03s          
  |+++++++++++++++++++++++++                         | 50% ~03s          
  |++++++++++++++++++++++++++                        | 51% ~03s          
  |+++++++++++++++++++++++++++                       | 52% ~03s          
  |+++++++++++++++++++++++++++                       | 53% ~03s          
  |++++++++++++++++++++++++++++                      | 54% ~03s          
  |++++++++++++++++++++++++++++                      | 55% ~03s          
  |+++++++++++++++++++++++++++++                     | 56% ~03s          
  |+++++++++++++++++++++++++++++                     | 57% ~03s          
  |++++++++++++++++++++++++++++++                    | 58% ~03s          
  |++++++++++++++++++++++++++++++                    | 59% ~02s          
  |+++++++++++++++++++++++++++++++                   | 60% ~02s          
  |+++++++++++++++++++++++++++++++                   | 61% ~02s          
  |++++++++++++++++++++++++++++++++                  | 62% ~02s          
  |++++++++++++++++++++++++++++++++                  | 63% ~02s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~02s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~02s          
  |++++++++++++++++++++++++++++++++++                | 66% ~02s          
  |++++++++++++++++++++++++++++++++++                | 67% ~02s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~02s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~02s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~02s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~02s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~02s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~02s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~02s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=06s  
Calculating cluster 6

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~10s          
  |++                                                | 2 % ~10s          
  |++                                                | 3 % ~09s          
  |+++                                               | 4 % ~09s          
  |+++                                               | 5 % ~09s          
  |++++                                              | 6 % ~09s          
  |++++                                              | 7 % ~09s          
  |+++++                                             | 8 % ~09s          
  |+++++                                             | 9 % ~09s          
  |++++++                                            | 10% ~09s          
  |++++++                                            | 11% ~09s          
  |+++++++                                           | 12% ~09s          
  |+++++++                                           | 13% ~08s          
  |++++++++                                          | 14% ~08s          
  |++++++++                                          | 15% ~08s          
  |+++++++++                                         | 16% ~08s          
  |+++++++++                                         | 17% ~08s          
  |++++++++++                                        | 18% ~08s          
  |++++++++++                                        | 19% ~08s          
  |+++++++++++                                       | 20% ~08s          
  |+++++++++++                                       | 21% ~08s          
  |++++++++++++                                      | 22% ~08s          
  |++++++++++++                                      | 23% ~08s          
  |+++++++++++++                                     | 24% ~07s          
  |+++++++++++++                                     | 25% ~07s          
  |++++++++++++++                                    | 26% ~07s          
  |++++++++++++++                                    | 27% ~07s          
  |+++++++++++++++                                   | 28% ~07s          
  |+++++++++++++++                                   | 29% ~07s          
  |++++++++++++++++                                  | 30% ~07s          
  |++++++++++++++++                                  | 31% ~07s          
  |+++++++++++++++++                                 | 32% ~07s          
  |+++++++++++++++++                                 | 33% ~07s          
  |++++++++++++++++++                                | 34% ~06s          
  |++++++++++++++++++                                | 35% ~06s          
  |+++++++++++++++++++                               | 36% ~06s          
  |+++++++++++++++++++                               | 37% ~06s          
  |++++++++++++++++++++                              | 38% ~06s          
  |++++++++++++++++++++                              | 39% ~06s          
  |+++++++++++++++++++++                             | 40% ~06s          
  |+++++++++++++++++++++                             | 41% ~06s          
  |++++++++++++++++++++++                            | 42% ~06s          
  |++++++++++++++++++++++                            | 43% ~06s          
  |+++++++++++++++++++++++                           | 44% ~05s          
  |+++++++++++++++++++++++                           | 45% ~05s          
  |++++++++++++++++++++++++                          | 46% ~05s          
  |++++++++++++++++++++++++                          | 47% ~05s          
  |+++++++++++++++++++++++++                         | 48% ~05s          
  |+++++++++++++++++++++++++                         | 49% ~05s          
  |++++++++++++++++++++++++++                        | 51% ~05s          
  |++++++++++++++++++++++++++                        | 52% ~05s          
  |+++++++++++++++++++++++++++                       | 53% ~05s          
  |+++++++++++++++++++++++++++                       | 54% ~05s          
  |++++++++++++++++++++++++++++                      | 55% ~04s          
  |++++++++++++++++++++++++++++                      | 56% ~04s          
  |+++++++++++++++++++++++++++++                     | 57% ~04s          
  |+++++++++++++++++++++++++++++                     | 58% ~04s          
  |++++++++++++++++++++++++++++++                    | 59% ~04s          
  |++++++++++++++++++++++++++++++                    | 60% ~04s          
  |+++++++++++++++++++++++++++++++                   | 61% ~04s          
  |+++++++++++++++++++++++++++++++                   | 62% ~04s          
  |++++++++++++++++++++++++++++++++                  | 63% ~04s          
  |++++++++++++++++++++++++++++++++                  | 64% ~04s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~03s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~03s          
  |++++++++++++++++++++++++++++++++++                | 67% ~03s          
  |++++++++++++++++++++++++++++++++++                | 68% ~03s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~03s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~03s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~03s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~03s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~03s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~03s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~02s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~02s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~02s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10s  
all_data.markers <- FindAllMarkers(all_data, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25) 
Calculating cluster 0

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~33s          
  |++                                                | 2 % ~33s          
  |++                                                | 3 % ~32s          
  |+++                                               | 4 % ~31s          
  |+++                                               | 5 % ~31s          
  |++++                                              | 6 % ~30s          
  |++++                                              | 7 % ~29s          
  |+++++                                             | 9 % ~28s          
  |+++++                                             | 10% ~28s          
  |++++++                                            | 11% ~28s          
  |++++++                                            | 12% ~27s          
  |+++++++                                           | 13% ~27s          
  |+++++++                                           | 14% ~27s          
  |++++++++                                          | 15% ~27s          
  |++++++++                                          | 16% ~26s          
  |+++++++++                                         | 17% ~26s          
  |++++++++++                                        | 18% ~26s          
  |++++++++++                                        | 19% ~26s          
  |+++++++++++                                       | 20% ~25s          
  |+++++++++++                                       | 21% ~28s          
  |++++++++++++                                      | 22% ~27s          
  |++++++++++++                                      | 23% ~27s          
  |+++++++++++++                                     | 24% ~26s          
  |+++++++++++++                                     | 26% ~26s          
  |++++++++++++++                                    | 27% ~26s          
  |++++++++++++++                                    | 28% ~25s          
  |+++++++++++++++                                   | 29% ~25s          
  |+++++++++++++++                                   | 30% ~24s          
  |++++++++++++++++                                  | 31% ~24s          
  |++++++++++++++++                                  | 32% ~23s          
  |+++++++++++++++++                                 | 33% ~23s          
  |++++++++++++++++++                                | 34% ~22s          
  |++++++++++++++++++                                | 35% ~22s          
  |+++++++++++++++++++                               | 36% ~22s          
  |+++++++++++++++++++                               | 37% ~21s          
  |++++++++++++++++++++                              | 38% ~21s          
  |++++++++++++++++++++                              | 39% ~20s          
  |+++++++++++++++++++++                             | 40% ~20s          
  |+++++++++++++++++++++                             | 41% ~20s          
  |++++++++++++++++++++++                            | 43% ~19s          
  |++++++++++++++++++++++                            | 44% ~19s          
  |+++++++++++++++++++++++                           | 45% ~19s          
  |+++++++++++++++++++++++                           | 46% ~18s          
  |++++++++++++++++++++++++                          | 47% ~18s          
  |++++++++++++++++++++++++                          | 48% ~18s          
  |+++++++++++++++++++++++++                         | 49% ~17s          
  |+++++++++++++++++++++++++                         | 50% ~17s          
  |++++++++++++++++++++++++++                        | 51% ~16s          
  |+++++++++++++++++++++++++++                       | 52% ~16s          
  |+++++++++++++++++++++++++++                       | 53% ~16s          
  |++++++++++++++++++++++++++++                      | 54% ~15s          
  |++++++++++++++++++++++++++++                      | 55% ~15s          
  |+++++++++++++++++++++++++++++                     | 56% ~15s          
  |+++++++++++++++++++++++++++++                     | 57% ~14s          
  |++++++++++++++++++++++++++++++                    | 59% ~14s          
  |++++++++++++++++++++++++++++++                    | 60% ~14s          
  |+++++++++++++++++++++++++++++++                   | 61% ~13s          
  |+++++++++++++++++++++++++++++++                   | 62% ~13s          
  |++++++++++++++++++++++++++++++++                  | 63% ~13s          
  |++++++++++++++++++++++++++++++++                  | 64% ~12s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~12s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~12s          
  |++++++++++++++++++++++++++++++++++                | 67% ~11s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~11s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~10s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~10s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~10s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~09s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~09s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~09s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~08s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~08s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~08s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~07s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~07s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~07s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=34s  
Calculating cluster 1

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~31s          
  |++                                                | 2 % ~29s          
  |++                                                | 3 % ~30s          
  |+++                                               | 4 % ~29s          
  |+++                                               | 5 % ~29s          
  |++++                                              | 7 % ~28s          
  |++++                                              | 8 % ~30s          
  |+++++                                             | 9 % ~29s          
  |+++++                                             | 10% ~29s          
  |++++++                                            | 11% ~28s          
  |+++++++                                           | 12% ~27s          
  |+++++++                                           | 13% ~27s          
  |++++++++                                          | 14% ~26s          
  |++++++++                                          | 15% ~26s          
  |+++++++++                                         | 16% ~26s          
  |+++++++++                                         | 18% ~25s          
  |++++++++++                                        | 19% ~25s          
  |++++++++++                                        | 20% ~24s          
  |+++++++++++                                       | 21% ~24s          
  |+++++++++++                                       | 22% ~24s          
  |++++++++++++                                      | 23% ~23s          
  |+++++++++++++                                     | 24% ~23s          
  |+++++++++++++                                     | 25% ~22s          
  |++++++++++++++                                    | 26% ~22s          
  |++++++++++++++                                    | 27% ~22s          
  |+++++++++++++++                                   | 29% ~21s          
  |+++++++++++++++                                   | 30% ~21s          
  |++++++++++++++++                                  | 31% ~21s          
  |++++++++++++++++                                  | 32% ~20s          
  |+++++++++++++++++                                 | 33% ~20s          
  |++++++++++++++++++                                | 34% ~20s          
  |++++++++++++++++++                                | 35% ~19s          
  |+++++++++++++++++++                               | 36% ~19s          
  |+++++++++++++++++++                               | 37% ~19s          
  |++++++++++++++++++++                              | 38% ~18s          
  |++++++++++++++++++++                              | 40% ~18s          
  |+++++++++++++++++++++                             | 41% ~18s          
  |+++++++++++++++++++++                             | 42% ~17s          
  |++++++++++++++++++++++                            | 43% ~17s          
  |++++++++++++++++++++++                            | 44% ~17s          
  |+++++++++++++++++++++++                           | 45% ~17s          
  |++++++++++++++++++++++++                          | 46% ~16s          
  |++++++++++++++++++++++++                          | 47% ~16s          
  |+++++++++++++++++++++++++                         | 48% ~16s          
  |+++++++++++++++++++++++++                         | 49% ~15s          
  |++++++++++++++++++++++++++                        | 51% ~15s          
  |++++++++++++++++++++++++++                        | 52% ~15s          
  |+++++++++++++++++++++++++++                       | 53% ~14s          
  |+++++++++++++++++++++++++++                       | 54% ~14s          
  |++++++++++++++++++++++++++++                      | 55% ~14s          
  |+++++++++++++++++++++++++++++                     | 56% ~13s          
  |+++++++++++++++++++++++++++++                     | 57% ~13s          
  |++++++++++++++++++++++++++++++                    | 58% ~13s          
  |++++++++++++++++++++++++++++++                    | 59% ~12s          
  |+++++++++++++++++++++++++++++++                   | 60% ~12s          
  |+++++++++++++++++++++++++++++++                   | 62% ~12s          
  |++++++++++++++++++++++++++++++++                  | 63% ~11s          
  |++++++++++++++++++++++++++++++++                  | 64% ~11s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~11s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~10s          
  |++++++++++++++++++++++++++++++++++                | 67% ~10s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~10s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~09s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~09s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~09s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~08s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~08s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~08s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~07s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~07s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~07s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=31s  
Calculating cluster 2

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~01m 10s      
  |++                                                | 2 % ~01m 10s      
  |++                                                | 3 % ~01m 08s      
  |+++                                               | 5 % ~01m 08s      
  |+++                                               | 6 % ~01m 07s      
  |++++                                              | 7 % ~01m 06s      
  |+++++                                             | 8 % ~01m 05s      
  |+++++                                             | 9 % ~01m 04s      
  |++++++                                            | 10% ~01m 03s      
  |++++++                                            | 11% ~01m 02s      
  |+++++++                                           | 13% ~01m 02s      
  |+++++++                                           | 14% ~01m 01s      
  |++++++++                                          | 15% ~01m 01s      
  |+++++++++                                         | 16% ~01m 00s      
  |+++++++++                                         | 17% ~60s          
  |++++++++++                                        | 18% ~59s          
  |++++++++++                                        | 20% ~58s          
  |+++++++++++                                       | 21% ~57s          
  |+++++++++++                                       | 22% ~56s          
  |++++++++++++                                      | 23% ~56s          
  |+++++++++++++                                     | 24% ~55s          
  |+++++++++++++                                     | 25% ~54s          
  |++++++++++++++                                    | 26% ~54s          
  |++++++++++++++                                    | 28% ~53s          
  |+++++++++++++++                                   | 29% ~52s          
  |+++++++++++++++                                   | 30% ~51s          
  |++++++++++++++++                                  | 31% ~50s          
  |+++++++++++++++++                                 | 32% ~49s          
  |+++++++++++++++++                                 | 33% ~49s          
  |++++++++++++++++++                                | 34% ~48s          
  |++++++++++++++++++                                | 36% ~47s          
  |+++++++++++++++++++                               | 37% ~46s          
  |+++++++++++++++++++                               | 38% ~45s          
  |++++++++++++++++++++                              | 39% ~45s          
  |+++++++++++++++++++++                             | 40% ~44s          
  |+++++++++++++++++++++                             | 41% ~43s          
  |++++++++++++++++++++++                            | 43% ~43s          
  |++++++++++++++++++++++                            | 44% ~42s          
  |+++++++++++++++++++++++                           | 45% ~41s          
  |+++++++++++++++++++++++                           | 46% ~40s          
  |++++++++++++++++++++++++                          | 47% ~39s          
  |+++++++++++++++++++++++++                         | 48% ~38s          
  |+++++++++++++++++++++++++                         | 49% ~37s          
  |++++++++++++++++++++++++++                        | 51% ~36s          
  |++++++++++++++++++++++++++                        | 52% ~35s          
  |+++++++++++++++++++++++++++                       | 53% ~35s          
  |++++++++++++++++++++++++++++                      | 54% ~34s          
  |++++++++++++++++++++++++++++                      | 55% ~33s          
  |+++++++++++++++++++++++++++++                     | 56% ~32s          
  |+++++++++++++++++++++++++++++                     | 57% ~31s          
  |++++++++++++++++++++++++++++++                    | 59% ~30s          
  |++++++++++++++++++++++++++++++                    | 60% ~29s          
  |+++++++++++++++++++++++++++++++                   | 61% ~29s          
  |++++++++++++++++++++++++++++++++                  | 62% ~28s          
  |++++++++++++++++++++++++++++++++                  | 63% ~27s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~26s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~25s          
  |++++++++++++++++++++++++++++++++++                | 67% ~25s          
  |++++++++++++++++++++++++++++++++++                | 68% ~24s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~23s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~22s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~21s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~20s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~19s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~19s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~18s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~17s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~16s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~15s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~14s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~14s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~13s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~12s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~11s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~10s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~09s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~09s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~08s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~07s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 94% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 14s
Calculating cluster 3

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~01m 09s      
  |++                                                | 2 % ~01m 07s      
  |++                                                | 3 % ~01m 07s      
  |+++                                               | 4 % ~01m 05s      
  |+++                                               | 5 % ~01m 05s      
  |++++                                              | 6 % ~01m 03s      
  |++++                                              | 7 % ~01m 02s      
  |+++++                                             | 8 % ~01m 01s      
  |+++++                                             | 9 % ~01m 00s      
  |++++++                                            | 10% ~01m 06s      
  |++++++                                            | 11% ~01m 05s      
  |+++++++                                           | 12% ~01m 03s      
  |+++++++                                           | 14% ~01m 02s      
  |++++++++                                          | 15% ~01m 01s      
  |++++++++                                          | 16% ~60s          
  |+++++++++                                         | 17% ~59s          
  |+++++++++                                         | 18% ~58s          
  |++++++++++                                        | 19% ~57s          
  |++++++++++                                        | 20% ~56s          
  |+++++++++++                                       | 21% ~55s          
  |+++++++++++                                       | 22% ~55s          
  |++++++++++++                                      | 23% ~54s          
  |++++++++++++                                      | 24% ~53s          
  |+++++++++++++                                     | 25% ~52s          
  |++++++++++++++                                    | 26% ~51s          
  |++++++++++++++                                    | 27% ~51s          
  |+++++++++++++++                                   | 28% ~50s          
  |+++++++++++++++                                   | 29% ~49s          
  |++++++++++++++++                                  | 30% ~48s          
  |++++++++++++++++                                  | 31% ~48s          
  |+++++++++++++++++                                 | 32% ~47s          
  |+++++++++++++++++                                 | 33% ~46s          
  |++++++++++++++++++                                | 34% ~46s          
  |++++++++++++++++++                                | 35% ~45s          
  |+++++++++++++++++++                               | 36% ~44s          
  |+++++++++++++++++++                               | 38% ~43s          
  |++++++++++++++++++++                              | 39% ~43s          
  |++++++++++++++++++++                              | 40% ~42s          
  |+++++++++++++++++++++                             | 41% ~41s          
  |+++++++++++++++++++++                             | 42% ~41s          
  |++++++++++++++++++++++                            | 43% ~40s          
  |++++++++++++++++++++++                            | 44% ~39s          
  |+++++++++++++++++++++++                           | 45% ~38s          
  |+++++++++++++++++++++++                           | 46% ~38s          
  |++++++++++++++++++++++++                          | 47% ~37s          
  |++++++++++++++++++++++++                          | 48% ~36s          
  |+++++++++++++++++++++++++                         | 49% ~35s          
  |+++++++++++++++++++++++++                         | 50% ~35s          
  |++++++++++++++++++++++++++                        | 51% ~34s          
  |+++++++++++++++++++++++++++                       | 52% ~33s          
  |+++++++++++++++++++++++++++                       | 53% ~33s          
  |++++++++++++++++++++++++++++                      | 54% ~32s          
  |++++++++++++++++++++++++++++                      | 55% ~31s          
  |+++++++++++++++++++++++++++++                     | 56% ~31s          
  |+++++++++++++++++++++++++++++                     | 57% ~30s          
  |++++++++++++++++++++++++++++++                    | 58% ~29s          
  |++++++++++++++++++++++++++++++                    | 59% ~28s          
  |+++++++++++++++++++++++++++++++                   | 60% ~28s          
  |+++++++++++++++++++++++++++++++                   | 61% ~27s          
  |++++++++++++++++++++++++++++++++                  | 62% ~26s          
  |++++++++++++++++++++++++++++++++                  | 64% ~25s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~25s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~24s          
  |++++++++++++++++++++++++++++++++++                | 67% ~23s          
  |++++++++++++++++++++++++++++++++++                | 68% ~22s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~22s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~21s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~20s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~19s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~19s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~18s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~17s          
  |+++++++++++++++++++++++++++++++++++++++           | 76% ~16s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~16s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~15s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~14s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~14s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~13s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~12s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~11s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~11s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~10s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~09s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~09s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~08s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~07s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 09s
Calculating cluster 4

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~01m 45s      
  |++                                                | 2 % ~01m 43s      
  |++                                                | 3 % ~01m 41s      
  |+++                                               | 4 % ~01m 40s      
  |+++                                               | 5 % ~01m 37s      
  |++++                                              | 6 % ~01m 34s      
  |++++                                              | 7 % ~01m 33s      
  |+++++                                             | 8 % ~01m 32s      
  |+++++                                             | 9 % ~01m 31s      
  |++++++                                            | 10% ~01m 30s      
  |++++++                                            | 11% ~01m 30s      
  |+++++++                                           | 12% ~01m 29s      
  |+++++++                                           | 13% ~01m 28s      
  |++++++++                                          | 14% ~01m 27s      
  |++++++++                                          | 15% ~01m 27s      
  |+++++++++                                         | 16% ~01m 26s      
  |+++++++++                                         | 17% ~01m 25s      
  |++++++++++                                        | 18% ~01m 24s      
  |++++++++++                                        | 19% ~01m 23s      
  |+++++++++++                                       | 20% ~01m 22s      
  |+++++++++++                                       | 21% ~01m 21s      
  |++++++++++++                                      | 22% ~01m 20s      
  |++++++++++++                                      | 23% ~01m 20s      
  |+++++++++++++                                     | 24% ~01m 18s      
  |+++++++++++++                                     | 26% ~01m 17s      
  |++++++++++++++                                    | 27% ~01m 16s      
  |++++++++++++++                                    | 28% ~01m 15s      
  |+++++++++++++++                                   | 29% ~01m 14s      
  |+++++++++++++++                                   | 30% ~01m 14s      
  |++++++++++++++++                                  | 31% ~01m 13s      
  |++++++++++++++++                                  | 32% ~01m 12s      
  |+++++++++++++++++                                 | 33% ~01m 10s      
  |+++++++++++++++++                                 | 34% ~01m 09s      
  |++++++++++++++++++                                | 35% ~01m 08s      
  |++++++++++++++++++                                | 36% ~01m 07s      
  |+++++++++++++++++++                               | 37% ~01m 06s      
  |+++++++++++++++++++                               | 38% ~01m 05s      
  |++++++++++++++++++++                              | 39% ~01m 03s      
  |++++++++++++++++++++                              | 40% ~01m 02s      
  |+++++++++++++++++++++                             | 41% ~01m 01s      
  |+++++++++++++++++++++                             | 42% ~01m 00s      
  |++++++++++++++++++++++                            | 43% ~59s          
  |++++++++++++++++++++++                            | 44% ~58s          
  |+++++++++++++++++++++++                           | 45% ~57s          
  |+++++++++++++++++++++++                           | 46% ~56s          
  |++++++++++++++++++++++++                          | 47% ~55s          
  |++++++++++++++++++++++++                          | 48% ~54s          
  |+++++++++++++++++++++++++                         | 49% ~53s          
  |+++++++++++++++++++++++++                         | 50% ~52s          
  |++++++++++++++++++++++++++                        | 51% ~51s          
  |+++++++++++++++++++++++++++                       | 52% ~50s          
  |+++++++++++++++++++++++++++                       | 53% ~49s          
  |++++++++++++++++++++++++++++                      | 54% ~48s          
  |++++++++++++++++++++++++++++                      | 55% ~47s          
  |+++++++++++++++++++++++++++++                     | 56% ~46s          
  |+++++++++++++++++++++++++++++                     | 57% ~45s          
  |++++++++++++++++++++++++++++++                    | 58% ~44s          
  |++++++++++++++++++++++++++++++                    | 59% ~43s          
  |+++++++++++++++++++++++++++++++                   | 60% ~42s          
  |+++++++++++++++++++++++++++++++                   | 61% ~41s          
  |++++++++++++++++++++++++++++++++                  | 62% ~40s          
  |++++++++++++++++++++++++++++++++                  | 63% ~39s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~38s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~37s          
  |++++++++++++++++++++++++++++++++++                | 66% ~35s          
  |++++++++++++++++++++++++++++++++++                | 67% ~34s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~33s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~32s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~31s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~30s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~29s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~28s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~27s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~26s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~25s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~24s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~22s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~21s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~20s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~19s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~18s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~17s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~16s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~15s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~14s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~13s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~12s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~11s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~10s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~09s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~08s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 45s
Calculating cluster 5

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~30s          
  |++                                                | 3 % ~30s          
  |++                                                | 4 % ~29s          
  |+++                                               | 5 % ~29s          
  |++++                                              | 7 % ~28s          
  |++++                                              | 8 % ~28s          
  |+++++                                             | 9 % ~28s          
  |++++++                                            | 11% ~27s          
  |++++++                                            | 12% ~27s          
  |+++++++                                           | 13% ~27s          
  |++++++++                                          | 14% ~26s          
  |++++++++                                          | 16% ~26s          
  |+++++++++                                         | 17% ~25s          
  |++++++++++                                        | 18% ~25s          
  |++++++++++                                        | 20% ~25s          
  |+++++++++++                                       | 21% ~24s          
  |++++++++++++                                      | 22% ~24s          
  |++++++++++++                                      | 24% ~24s          
  |+++++++++++++                                     | 25% ~23s          
  |++++++++++++++                                    | 26% ~23s          
  |++++++++++++++                                    | 28% ~23s          
  |+++++++++++++++                                   | 29% ~22s          
  |++++++++++++++++                                  | 30% ~22s          
  |++++++++++++++++                                  | 32% ~21s          
  |+++++++++++++++++                                 | 33% ~21s          
  |++++++++++++++++++                                | 34% ~21s          
  |++++++++++++++++++                                | 36% ~20s          
  |+++++++++++++++++++                               | 37% ~20s          
  |++++++++++++++++++++                              | 38% ~20s          
  |++++++++++++++++++++                              | 39% ~19s          
  |+++++++++++++++++++++                             | 41% ~19s          
  |++++++++++++++++++++++                            | 42% ~19s          
  |++++++++++++++++++++++                            | 43% ~18s          
  |+++++++++++++++++++++++                           | 45% ~18s          
  |++++++++++++++++++++++++                          | 46% ~17s          
  |++++++++++++++++++++++++                          | 47% ~17s          
  |+++++++++++++++++++++++++                         | 49% ~17s          
  |+++++++++++++++++++++++++                         | 50% ~16s          
  |++++++++++++++++++++++++++                        | 51% ~16s          
  |+++++++++++++++++++++++++++                       | 53% ~16s          
  |+++++++++++++++++++++++++++                       | 54% ~16s          
  |++++++++++++++++++++++++++++                      | 55% ~15s          
  |+++++++++++++++++++++++++++++                     | 57% ~15s          
  |+++++++++++++++++++++++++++++                     | 58% ~14s          
  |++++++++++++++++++++++++++++++                    | 59% ~14s          
  |+++++++++++++++++++++++++++++++                   | 61% ~13s          
  |+++++++++++++++++++++++++++++++                   | 62% ~13s          
  |++++++++++++++++++++++++++++++++                  | 63% ~13s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~12s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~12s          
  |++++++++++++++++++++++++++++++++++                | 67% ~11s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~11s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~10s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~10s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~09s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~09s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~08s          
  |+++++++++++++++++++++++++++++++++++++++           | 76% ~08s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~08s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~07s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~07s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=34s  
Calculating cluster 6

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~01m 04s      
  |++                                                | 2 % ~01m 02s      
  |++                                                | 4 % ~01m 01s      
  |+++                                               | 5 % ~01m 01s      
  |+++                                               | 6 % ~60s          
  |++++                                              | 7 % ~59s          
  |+++++                                             | 8 % ~58s          
  |+++++                                             | 10% ~01m 00s      
  |++++++                                            | 11% ~59s          
  |++++++                                            | 12% ~58s          
  |+++++++                                           | 13% ~57s          
  |++++++++                                          | 14% ~56s          
  |++++++++                                          | 15% ~55s          
  |+++++++++                                         | 17% ~54s          
  |+++++++++                                         | 18% ~53s          
  |++++++++++                                        | 19% ~52s          
  |+++++++++++                                       | 20% ~52s          
  |+++++++++++                                       | 21% ~51s          
  |++++++++++++                                      | 23% ~50s          
  |++++++++++++                                      | 24% ~50s          
  |+++++++++++++                                     | 25% ~49s          
  |++++++++++++++                                    | 26% ~48s          
  |++++++++++++++                                    | 27% ~47s          
  |+++++++++++++++                                   | 29% ~47s          
  |+++++++++++++++                                   | 30% ~46s          
  |++++++++++++++++                                  | 31% ~45s          
  |+++++++++++++++++                                 | 32% ~44s          
  |+++++++++++++++++                                 | 33% ~44s          
  |++++++++++++++++++                                | 35% ~43s          
  |++++++++++++++++++                                | 36% ~42s          
  |+++++++++++++++++++                               | 37% ~41s          
  |++++++++++++++++++++                              | 38% ~41s          
  |++++++++++++++++++++                              | 39% ~40s          
  |+++++++++++++++++++++                             | 40% ~39s          
  |+++++++++++++++++++++                             | 42% ~38s          
  |++++++++++++++++++++++                            | 43% ~38s          
  |+++++++++++++++++++++++                           | 44% ~37s          
  |+++++++++++++++++++++++                           | 45% ~36s          
  |++++++++++++++++++++++++                          | 46% ~35s          
  |++++++++++++++++++++++++                          | 48% ~35s          
  |+++++++++++++++++++++++++                         | 49% ~34s          
  |+++++++++++++++++++++++++                         | 50% ~33s          
  |++++++++++++++++++++++++++                        | 51% ~33s          
  |+++++++++++++++++++++++++++                       | 52% ~32s          
  |+++++++++++++++++++++++++++                       | 54% ~31s          
  |++++++++++++++++++++++++++++                      | 55% ~30s          
  |++++++++++++++++++++++++++++                      | 56% ~29s          
  |+++++++++++++++++++++++++++++                     | 57% ~29s          
  |++++++++++++++++++++++++++++++                    | 58% ~28s          
  |++++++++++++++++++++++++++++++                    | 60% ~27s          
  |+++++++++++++++++++++++++++++++                   | 61% ~26s          
  |+++++++++++++++++++++++++++++++                   | 62% ~25s          
  |++++++++++++++++++++++++++++++++                  | 63% ~24s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~24s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~23s          
  |++++++++++++++++++++++++++++++++++                | 67% ~22s          
  |++++++++++++++++++++++++++++++++++                | 68% ~21s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~20s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~20s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~19s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~18s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~17s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~17s          
  |+++++++++++++++++++++++++++++++++++++++           | 76% ~16s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~15s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~14s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~13s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~13s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~12s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~11s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~10s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~10s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~09s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~08s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~07s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 94% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 08s
Calculating cluster 7

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~14s          
  |++                                                | 3 % ~14s          
  |++                                                | 4 % ~14s          
  |+++                                               | 5 % ~14s          
  |++++                                              | 7 % ~13s          
  |++++                                              | 8 % ~13s          
  |+++++                                             | 9 % ~13s          
  |++++++                                            | 11% ~12s          
  |++++++                                            | 12% ~12s          
  |+++++++                                           | 13% ~12s          
  |++++++++                                          | 15% ~12s          
  |++++++++                                          | 16% ~12s          
  |+++++++++                                         | 17% ~12s          
  |++++++++++                                        | 19% ~11s          
  |++++++++++                                        | 20% ~11s          
  |+++++++++++                                       | 21% ~11s          
  |++++++++++++                                      | 23% ~11s          
  |++++++++++++                                      | 24% ~11s          
  |+++++++++++++                                     | 25% ~10s          
  |++++++++++++++                                    | 27% ~10s          
  |++++++++++++++                                    | 28% ~10s          
  |+++++++++++++++                                   | 29% ~10s          
  |++++++++++++++++                                  | 31% ~10s          
  |++++++++++++++++                                  | 32% ~09s          
  |+++++++++++++++++                                 | 33% ~09s          
  |++++++++++++++++++                                | 35% ~09s          
  |++++++++++++++++++                                | 36% ~09s          
  |+++++++++++++++++++                               | 37% ~09s          
  |++++++++++++++++++++                              | 39% ~09s          
  |++++++++++++++++++++                              | 40% ~08s          
  |+++++++++++++++++++++                             | 41% ~08s          
  |++++++++++++++++++++++                            | 43% ~08s          
  |++++++++++++++++++++++                            | 44% ~08s          
  |+++++++++++++++++++++++                           | 45% ~08s          
  |++++++++++++++++++++++++                          | 47% ~07s          
  |++++++++++++++++++++++++                          | 48% ~07s          
  |+++++++++++++++++++++++++                         | 49% ~07s          
  |++++++++++++++++++++++++++                        | 51% ~07s          
  |++++++++++++++++++++++++++                        | 52% ~07s          
  |+++++++++++++++++++++++++++                       | 53% ~07s          
  |++++++++++++++++++++++++++++                      | 55% ~06s          
  |++++++++++++++++++++++++++++                      | 56% ~06s          
  |+++++++++++++++++++++++++++++                     | 57% ~06s          
  |++++++++++++++++++++++++++++++                    | 59% ~06s          
  |++++++++++++++++++++++++++++++                    | 60% ~06s          
  |+++++++++++++++++++++++++++++++                   | 61% ~05s          
  |++++++++++++++++++++++++++++++++                  | 63% ~05s          
  |++++++++++++++++++++++++++++++++                  | 64% ~05s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~05s          
  |++++++++++++++++++++++++++++++++++                | 67% ~05s          
  |++++++++++++++++++++++++++++++++++                | 68% ~05s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~04s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~04s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~04s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~04s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~04s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~03s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~03s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~03s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=15s  
Calculating cluster 8

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~01m 38s      
  |++                                                | 2 % ~01m 39s      
  |++                                                | 3 % ~01m 37s      
  |+++                                               | 4 % ~01m 42s      
  |+++                                               | 5 % ~01m 38s      
  |++++                                              | 6 % ~01m 36s      
  |++++                                              | 7 % ~01m 35s      
  |+++++                                             | 8 % ~01m 34s      
  |+++++                                             | 9 % ~01m 32s      
  |++++++                                            | 10% ~01m 31s      
  |++++++                                            | 11% ~01m 31s      
  |+++++++                                           | 12% ~01m 30s      
  |+++++++                                           | 14% ~01m 29s      
  |++++++++                                          | 15% ~01m 29s      
  |++++++++                                          | 16% ~01m 27s      
  |+++++++++                                         | 17% ~01m 26s      
  |+++++++++                                         | 18% ~01m 25s      
  |++++++++++                                        | 19% ~01m 24s      
  |++++++++++                                        | 20% ~01m 23s      
  |+++++++++++                                       | 21% ~01m 22s      
  |+++++++++++                                       | 22% ~01m 21s      
  |++++++++++++                                      | 23% ~01m 20s      
  |++++++++++++                                      | 24% ~01m 19s      
  |+++++++++++++                                     | 25% ~01m 18s      
  |++++++++++++++                                    | 26% ~01m 17s      
  |++++++++++++++                                    | 27% ~01m 17s      
  |+++++++++++++++                                   | 28% ~01m 15s      
  |+++++++++++++++                                   | 29% ~01m 14s      
  |++++++++++++++++                                  | 30% ~01m 13s      
  |++++++++++++++++                                  | 31% ~01m 11s      
  |+++++++++++++++++                                 | 32% ~01m 10s      
  |+++++++++++++++++                                 | 33% ~01m 09s      
  |++++++++++++++++++                                | 34% ~01m 07s      
  |++++++++++++++++++                                | 35% ~01m 06s      
  |+++++++++++++++++++                               | 36% ~01m 05s      
  |+++++++++++++++++++                               | 38% ~01m 04s      
  |++++++++++++++++++++                              | 39% ~01m 03s      
  |++++++++++++++++++++                              | 40% ~01m 02s      
  |+++++++++++++++++++++                             | 41% ~01m 01s      
  |+++++++++++++++++++++                             | 42% ~01m 00s      
  |++++++++++++++++++++++                            | 43% ~59s          
  |++++++++++++++++++++++                            | 44% ~58s          
  |+++++++++++++++++++++++                           | 45% ~57s          
  |+++++++++++++++++++++++                           | 46% ~56s          
  |++++++++++++++++++++++++                          | 47% ~55s          
  |++++++++++++++++++++++++                          | 48% ~54s          
  |+++++++++++++++++++++++++                         | 49% ~53s          
  |+++++++++++++++++++++++++                         | 50% ~52s          
  |++++++++++++++++++++++++++                        | 51% ~51s          
  |+++++++++++++++++++++++++++                       | 52% ~50s          
  |+++++++++++++++++++++++++++                       | 53% ~49s          
  |++++++++++++++++++++++++++++                      | 54% ~47s          
  |++++++++++++++++++++++++++++                      | 55% ~46s          
  |+++++++++++++++++++++++++++++                     | 56% ~45s          
  |+++++++++++++++++++++++++++++                     | 57% ~44s          
  |++++++++++++++++++++++++++++++                    | 58% ~43s          
  |++++++++++++++++++++++++++++++                    | 59% ~42s          
  |+++++++++++++++++++++++++++++++                   | 60% ~41s          
  |+++++++++++++++++++++++++++++++                   | 61% ~40s          
  |++++++++++++++++++++++++++++++++                  | 62% ~39s          
  |++++++++++++++++++++++++++++++++                  | 64% ~38s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~37s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~35s          
  |++++++++++++++++++++++++++++++++++                | 67% ~34s          
  |++++++++++++++++++++++++++++++++++                | 68% ~33s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~32s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~31s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~30s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~29s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~28s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~27s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~26s          
  |+++++++++++++++++++++++++++++++++++++++           | 76% ~25s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~24s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~23s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~22s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~20s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~19s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~18s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~17s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~16s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~15s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~14s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~13s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~12s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~11s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~10s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~09s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~08s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 44s
Calculating cluster 9

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~02m 18s      
  |++                                                | 2 % ~02m 18s      
  |++                                                | 3 % ~02m 14s      
  |+++                                               | 4 % ~02m 12s      
  |+++                                               | 5 % ~02m 11s      
  |++++                                              | 6 % ~02m 10s      
  |++++                                              | 7 % ~02m 09s      
  |+++++                                             | 9 % ~02m 09s      
  |+++++                                             | 10% ~02m 08s      
  |++++++                                            | 11% ~02m 07s      
  |++++++                                            | 12% ~02m 06s      
  |+++++++                                           | 13% ~02m 05s      
  |+++++++                                           | 14% ~02m 03s      
  |++++++++                                          | 15% ~02m 03s      
  |++++++++                                          | 16% ~02m 01s      
  |+++++++++                                         | 17% ~01m 59s      
  |++++++++++                                        | 18% ~01m 58s      
  |++++++++++                                        | 19% ~01m 56s      
  |+++++++++++                                       | 20% ~01m 54s      
  |+++++++++++                                       | 21% ~01m 52s      
  |++++++++++++                                      | 22% ~01m 51s      
  |++++++++++++                                      | 23% ~01m 49s      
  |+++++++++++++                                     | 24% ~01m 48s      
  |+++++++++++++                                     | 26% ~01m 46s      
  |++++++++++++++                                    | 27% ~01m 45s      
  |++++++++++++++                                    | 28% ~01m 43s      
  |+++++++++++++++                                   | 29% ~01m 42s      
  |+++++++++++++++                                   | 30% ~01m 40s      
  |++++++++++++++++                                  | 31% ~01m 39s      
  |++++++++++++++++                                  | 32% ~01m 38s      
  |+++++++++++++++++                                 | 33% ~01m 36s      
  |++++++++++++++++++                                | 34% ~01m 34s      
  |++++++++++++++++++                                | 35% ~01m 33s      
  |+++++++++++++++++++                               | 36% ~01m 31s      
  |+++++++++++++++++++                               | 37% ~01m 29s      
  |++++++++++++++++++++                              | 38% ~01m 28s      
  |++++++++++++++++++++                              | 39% ~01m 26s      
  |+++++++++++++++++++++                             | 40% ~01m 25s      
  |+++++++++++++++++++++                             | 41% ~01m 23s      
  |++++++++++++++++++++++                            | 43% ~01m 22s      
  |++++++++++++++++++++++                            | 44% ~01m 21s      
  |+++++++++++++++++++++++                           | 45% ~01m 19s      
  |+++++++++++++++++++++++                           | 46% ~01m 18s      
  |++++++++++++++++++++++++                          | 47% ~01m 17s      
  |++++++++++++++++++++++++                          | 48% ~01m 15s      
  |+++++++++++++++++++++++++                         | 49% ~01m 13s      
  |+++++++++++++++++++++++++                         | 50% ~01m 12s      
  |++++++++++++++++++++++++++                        | 51% ~01m 10s      
  |+++++++++++++++++++++++++++                       | 52% ~01m 08s      
  |+++++++++++++++++++++++++++                       | 53% ~01m 07s      
  |++++++++++++++++++++++++++++                      | 54% ~01m 05s      
  |++++++++++++++++++++++++++++                      | 55% ~01m 04s      
  |+++++++++++++++++++++++++++++                     | 56% ~01m 02s      
  |+++++++++++++++++++++++++++++                     | 57% ~01m 01s      
  |++++++++++++++++++++++++++++++                    | 59% ~59s          
  |++++++++++++++++++++++++++++++                    | 60% ~58s          
  |+++++++++++++++++++++++++++++++                   | 61% ~56s          
  |+++++++++++++++++++++++++++++++                   | 62% ~55s          
  |++++++++++++++++++++++++++++++++                  | 63% ~53s          
  |++++++++++++++++++++++++++++++++                  | 64% ~52s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~50s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~49s          
  |++++++++++++++++++++++++++++++++++                | 67% ~47s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~46s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~44s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~43s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~41s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~39s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~38s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~36s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~35s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~33s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~32s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~30s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~29s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~27s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~26s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~24s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~23s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~21s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~20s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~18s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~17s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~15s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~14s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~12s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~11s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~09s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~08s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 22s
Calculating cluster 10

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~45s          
  |++                                                | 2 % ~46s          
  |++                                                | 3 % ~45s          
  |+++                                               | 4 % ~45s          
  |+++                                               | 5 % ~44s          
  |++++                                              | 7 % ~43s          
  |++++                                              | 8 % ~43s          
  |+++++                                             | 9 % ~42s          
  |+++++                                             | 10% ~41s          
  |++++++                                            | 11% ~41s          
  |++++++                                            | 12% ~40s          
  |+++++++                                           | 13% ~40s          
  |++++++++                                          | 14% ~40s          
  |++++++++                                          | 15% ~39s          
  |+++++++++                                         | 16% ~39s          
  |+++++++++                                         | 17% ~39s          
  |++++++++++                                        | 18% ~38s          
  |++++++++++                                        | 20% ~38s          
  |+++++++++++                                       | 21% ~38s          
  |+++++++++++                                       | 22% ~37s          
  |++++++++++++                                      | 23% ~37s          
  |++++++++++++                                      | 24% ~36s          
  |+++++++++++++                                     | 25% ~36s          
  |++++++++++++++                                    | 26% ~36s          
  |++++++++++++++                                    | 27% ~35s          
  |+++++++++++++++                                   | 28% ~35s          
  |+++++++++++++++                                   | 29% ~34s          
  |++++++++++++++++                                  | 30% ~34s          
  |++++++++++++++++                                  | 32% ~33s          
  |+++++++++++++++++                                 | 33% ~33s          
  |+++++++++++++++++                                 | 34% ~32s          
  |++++++++++++++++++                                | 35% ~32s          
  |++++++++++++++++++                                | 36% ~31s          
  |+++++++++++++++++++                               | 37% ~31s          
  |++++++++++++++++++++                              | 38% ~31s          
  |++++++++++++++++++++                              | 39% ~30s          
  |+++++++++++++++++++++                             | 40% ~30s          
  |+++++++++++++++++++++                             | 41% ~29s          
  |++++++++++++++++++++++                            | 42% ~28s          
  |++++++++++++++++++++++                            | 43% ~28s          
  |+++++++++++++++++++++++                           | 45% ~27s          
  |+++++++++++++++++++++++                           | 46% ~27s          
  |++++++++++++++++++++++++                          | 47% ~26s          
  |++++++++++++++++++++++++                          | 48% ~26s          
  |+++++++++++++++++++++++++                         | 49% ~25s          
  |+++++++++++++++++++++++++                         | 50% ~24s          
  |++++++++++++++++++++++++++                        | 51% ~24s          
  |+++++++++++++++++++++++++++                       | 52% ~23s          
  |+++++++++++++++++++++++++++                       | 53% ~23s          
  |++++++++++++++++++++++++++++                      | 54% ~22s          
  |++++++++++++++++++++++++++++                      | 55% ~22s          
  |+++++++++++++++++++++++++++++                     | 57% ~21s          
  |+++++++++++++++++++++++++++++                     | 58% ~21s          
  |++++++++++++++++++++++++++++++                    | 59% ~20s          
  |++++++++++++++++++++++++++++++                    | 60% ~20s          
  |+++++++++++++++++++++++++++++++                   | 61% ~19s          
  |+++++++++++++++++++++++++++++++                   | 62% ~19s          
  |++++++++++++++++++++++++++++++++                  | 63% ~18s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~18s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~17s          
  |++++++++++++++++++++++++++++++++++                | 66% ~16s          
  |++++++++++++++++++++++++++++++++++                | 67% ~16s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~15s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~15s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~14s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~14s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~13s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~13s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~12s          
  |+++++++++++++++++++++++++++++++++++++++           | 76% ~12s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~11s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~11s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~10s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~10s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~09s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~09s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~08s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~08s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~07s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=50s  
Calculating cluster 11

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~02m 58s      
  |++                                                | 2 % ~02m 58s      
  |++                                                | 3 % ~03m 01s      
  |+++                                               | 4 % ~02m 55s      
  |+++                                               | 5 % ~02m 50s      
  |++++                                              | 6 % ~02m 48s      
  |++++                                              | 7 % ~02m 46s      
  |+++++                                             | 8 % ~02m 45s      
  |+++++                                             | 9 % ~02m 43s      
  |++++++                                            | 11% ~02m 42s      
  |++++++                                            | 12% ~02m 40s      
  |+++++++                                           | 13% ~02m 39s      
  |+++++++                                           | 14% ~02m 37s      
  |++++++++                                          | 15% ~02m 36s      
  |++++++++                                          | 16% ~02m 34s      
  |+++++++++                                         | 17% ~02m 34s      
  |+++++++++                                         | 18% ~02m 32s      
  |++++++++++                                        | 19% ~02m 29s      
  |++++++++++                                        | 20% ~02m 27s      
  |+++++++++++                                       | 21% ~02m 24s      
  |++++++++++++                                      | 22% ~02m 22s      
  |++++++++++++                                      | 23% ~02m 20s      
  |+++++++++++++                                     | 24% ~02m 18s      
  |+++++++++++++                                     | 25% ~02m 16s      
  |++++++++++++++                                    | 26% ~02m 14s      
  |++++++++++++++                                    | 27% ~02m 12s      
  |+++++++++++++++                                   | 28% ~02m 10s      
  |+++++++++++++++                                   | 29% ~02m 09s      
  |++++++++++++++++                                  | 31% ~02m 07s      
  |++++++++++++++++                                  | 32% ~02m 04s      
  |+++++++++++++++++                                 | 33% ~02m 02s      
  |+++++++++++++++++                                 | 34% ~02m 00s      
  |++++++++++++++++++                                | 35% ~01m 58s      
  |++++++++++++++++++                                | 36% ~01m 56s      
  |+++++++++++++++++++                               | 37% ~01m 54s      
  |+++++++++++++++++++                               | 38% ~01m 52s      
  |++++++++++++++++++++                              | 39% ~01m 50s      
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  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03m 00s
save(all_data.markers, cis_markers, cocl2_markers, dabtram_markers, file = 'all_data_markers.RData')
#find lineages that are maintained at both dabtram timepoints
fivecell_cDNA$DabTramMaintained <- Reduce(intersect, list(fivecell_cDNA$DabTram, fivecell_cDNA$DabTramtoDabTram))

filtered_meta <- rep(0, length(names(all_data$Lineage)))

#specify which cells are in lineages that pass filtering for that condition
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% combined_lins_list$DabTram)] <- 'Resistant to DabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% combined_lins_list$DabTramtoDabTram)] <- 'Resistant to DabTramtoDabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %in% combined_lins_list$DabTramtoCoCl2)] <- 'Resistant to DabTramtoCoCl2'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %in% combined_lins_list$DabTramtoCis)] <- 'Resistant to DabTramtoCis'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %in% combined_lins_list$CoCl2)] <- 'Resistant to CoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %in% combined_lins_list$CoCl2toDabTram)] <- 'Resistant to CoCl2toDabTram'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %in% combined_lins_list$CoCl2toCoCl2)] <- 'Resistant to CoCl2toCoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %in% combined_lins_list$CoCl2toCis)] <- 'Resistant to CoCl2toCis'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %in% combined_lins_list$Cis)] <- 'Resistant to Cis'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %in% combined_lins_list$CistoDabTram)] <- 'Resistant to CistoDabTram'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %in% combined_lins_list$CistoCoCl2)] <- 'Resistant to CistoCoCl2'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %in% combined_lins_list$CistoCis)] <- 'Resistant to CistoCis'

#specify which cells are in lineages of more than 5 cells
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% fivecell_cDNA$DabTram)] <- 'Large Resistant to DabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramtoDabTram)] <- 'Large Resistant to DabTramtoDabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %in% fivecell_cDNA$DabTramtoCoCl2)] <- 'Large Resistant to DabTramtoCoCl2'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %in% fivecell_cDNA$DabTramtoCis)] <- 'Large Resistant to DabTramtoCis'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %in% fivecell_cDNA$CoCl2)] <- 'Large Resistant to CoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %in% fivecell_cDNA$CoCl2toDabTram)] <- 'Large Resistant to CoCl2toDabTram'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %in% fivecell_cDNA$CoCl2toCoCl2)] <- 'Large Resistant to CoCl2toCoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %in% fivecell_cDNA$CoCl2toCis)] <- 'Large Resistant to CoCl2toCis'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %in% fivecell_cDNA$Cis)] <- 'Large Resistant to Cis'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %in% fivecell_cDNA$CistoDabTram)] <- 'Large Resistant to CistoDabTram'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %in% fivecell_cDNA$CistoCoCl2)] <- 'Large Resistant to CistoCoCl2'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %in% fivecell_cDNA$CistoCis)] <- 'Large Resistant to CistoCis'

# filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramMaintained)] <- 'Maintained Resistant to DabTram'
# filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramMaintained)] <- 'Maintained Resistant to DabTramtoDabTram'

#specify which cells are in lineages that did not pass filtering
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %nin% combined_lins_list$DabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %nin% combined_lins_list$DabTramtoDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %nin% combined_lins_list$DabTramtoCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %nin% combined_lins_list$DabTramtoCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %nin% combined_lins_list$CoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %nin% combined_lins_list$CoCl2toDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %nin% combined_lins_list$CoCl2toCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %nin% combined_lins_list$CoCl2toCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %nin% combined_lins_list$Cis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %nin% combined_lins_list$CistoDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %nin% combined_lins_list$CistoCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %nin% combined_lins_list$CistoCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 

#specify which cells had zero or multiple barcodes
filtered_meta[which(all_data$Lineage %in% c("No Barcode", "Still multiple"))] <- 'No Barcode'

print(table(filtered_meta))
filtered_meta
                       Filtered out              Large Resistant to Cis         Large Resistant to CistoCis       Large Resistant to CistoCoCl2 
                               3337                                1375                                 951                                2078 
    Large Resistant to CistoDabTram            Large Resistant to CoCl2       Large Resistant to CoCl2toCis     Large Resistant to CoCl2toCoCl2 
                               1394                                1784                                3010                               11578 
  Large Resistant to CoCl2toDabTram          Large Resistant to DabTram     Large Resistant to DabTramtoCis   Large Resistant to DabTramtoCoCl2 
                                663                                 478                                4234                                2840 
Large Resistant to DabTramtoDabTram                          No Barcode                    Resistant to Cis               Resistant to CistoCis 
                               3176                               35314                                 331                                  67 
            Resistant to CistoCoCl2           Resistant to CistoDabTram                  Resistant to CoCl2             Resistant to CoCl2toCis 
                                135                                 113                                 278                                 157 
          Resistant to CoCl2toCoCl2         Resistant to CoCl2toDabTram                Resistant to DabTram           Resistant to DabTramtoCis 
                                 55                                  93                                 337                                 225 
        Resistant to DabTramtoCoCl2       Resistant to DabTramtoDabTram 
                                100                                 222 
all_data$Resistant_filtered <- filtered_meta
Idents(all_data) <- all_data$Resistant_filtered
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/test_plot.pdf', height = 10, width = 20)
DimPlot(all_data, group.by = 'ident', cols = )
DimPlot(all_data, group.by = "Lineage") + theme(legend.position = 'none')
dev.off()
null device 
          1 

#Looking into the one lineage that switches ngfr -> egfr

pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/test_violin.pdf', height = 10, width = 30)

VlnPlot(all_data, features = 'NGFR', idents = 'Maintained Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents = idents,  :
  All cells have the same value of NGFR.
Warning in max(data[, feature][is.finite(x = data[, feature])]) :
  no non-missing arguments to max; returning -Inf
Warning in min(data[, feature]) :
  no non-missing arguments to min; returning Inf
VlnPlot(all_data, features = 'EGFR', idents = 'Maintained Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents = idents,  :
  All cells have the same value of EGFR.
Warning in max(data[, feature][is.finite(x = data[, feature])]) :
  no non-missing arguments to max; returning -Inf
Warning in min(data[, feature]) :
  no non-missing arguments to min; returning Inf
VlnPlot(all_data, features = 'NGFR', idents = 'Maintained Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents = idents,  :
  All cells have the same value of NGFR.
Warning in max(data[, feature][is.finite(x = data[, feature])]) :
  no non-missing arguments to max; returning -Inf
Warning in min(data[, feature]) :
  no non-missing arguments to min; returning Inf
VlnPlot(all_data, features = 'EGFR', idents = 'Maintained Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents = idents,  :
  All cells have the same value of EGFR.
Warning in max(data[, feature][is.finite(x = data[, feature])]) :
  no non-missing arguments to max; returning -Inf
Warning in min(data[, feature]) :
  no non-missing arguments to min; returning Inf
VlnPlot(all_data, features = 'NGFR', idents = 'Large Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'EGFR', idents = 'Large Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'NGFR', idents = 'Large Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'EGFR', idents = 'Large Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)

dev.off()
null device 
          1 
#VlnPlot(all_data, features = 'NGFR', idents = all_data$OG_condition == 'dabtram', group.by = "lineage")

#from dabtram_both_times, force 2 clusters in umap, plot vs ngfr egfr, find markers of these 2


switch_lin_dabtram <- names(all_data$orig.ident[all_data$Lineage == "Lin171516" & all_data$OG_condition == 'dabtram'])
switch_lin_dabtramtodabtram <- names(all_data$orig.ident[all_data$Lineage == "Lin171516" & all_data$OG_condition == 'dabtramtodabtram'])

DimPlot(all_data, group.by = "OG_condition", cols = c('dabtram' = '#623594', 'cocl2' = '#0F8241', 'cis' = '#C96D29', 'dabtramtodabtram' = '#561E59', 'dabtramtococl2' = '#A2248E', 'dabtramtocis' = '#9D85BE', 'cocl2todabtram' = '#10413B', 'cocl2tococl2' = '#6ABD45', 'cocl2tocis' = '#6DC49C', 'cistodabtram' = '#A23622', 'cistococl2' = '#F49129', 'cistocis' = '#FBD08C'))

DimPlot(all_data, cells.highlight = list(switch_lin_dabtram), cols.highlight = c('red'))

DimPlot(all_data, cells.highlight = list(switch_lin_dabtram, switch_lin_dabtramtodabtram), cols.highlight = c('blue', 'red'))


switch_lin <- names(dabtram$orig.ident[dabtram$Lineage == "Lin171516"])
DimPlot(dabtram)

DimPlot(dabtram, cells.highlight = list(switch_lin))

#Assign cluster assignments per lineage, find average score per lineage - make plots in order


dabtram_both_times_markers <- FindAllMarkers(dabtram_both_times, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
Calculating cluster 0

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~19s          
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  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=19s  
Calculating cluster 1

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~20s          
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  |+++++++++++++++++++++++                           | 45% ~13s          
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  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=22s  
DimPlot(dabtram_both_times)

DimPlot(dabtram_both_times, group.by = 'OG_condition')

FeaturePlot(dabtram_both_times, features = c('NGFR', 'EGFR', 'nFeature_RNA'))

Plot the number of cells per lineage in second drug for lineages that survived in DabTram

#average cell assignments per lineage in dabtram_maintained

#want to do a stacked barplot here-- copy paste code from condition clustering

#get lineage and cluster data from seurat object, switch cluster identifiers from 0,1 to -1,1 (egfr, ngfr)
clusters_per_lin <- data.frame (Cluster = as.numeric(as.character(dabtram_both_times$seurat_clusters)), Lineage = dabtram_both_times$Lineage, condition = dabtram_both_times$OG_condition)
clusters_per_lin$Cluster[clusters_per_lin$Cluster == 0] <- -1
clusters_per_lin_list <- list()

# Need to get percent values of EGFR for the lineage after the first treatment
for (i in fivecell_cDNA$DabTram){
  currentlin <- filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtram')
  Var1 <- c(-1,1)
  Freq <- c(sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtram')$Cluster == -1), # EGFR
  sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtram')$Cluster == 1))
  clusters_per_lin_list[[i]] <- data.frame('Var1' = Var1, 'Freq' = Freq)
  clusters_per_lin_list[[i]]$Score <- weighted.mean(as.numeric(as.character(clusters_per_lin_list[[i]]$Var1)), clusters_per_lin_list[[i]]$Freq)
}

# Build a dataframe for plotting based on % EGFR or NGFR per lineage after first treatment
clusters_per_lin_df <- data.frame(matrix(ncol = 5, nrow = 0))
colnames(clusters_per_lin_df) <- c('Lineage', 'Percent_cells', 'Num_cells', 'Score')
for (i in names(clusters_per_lin_list)){
    clusters_per_lin_df <- rbind(clusters_per_lin_df, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == 1]/sum(clusters_per_lin_list[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == 1]), 'Score' = clusters_per_lin_list[[i]]$Score[1], 'Cluster' = 'NGFR')) # Add NGFR row
  clusters_per_lin_df <- rbind(clusters_per_lin_df, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == -1]/sum(clusters_per_lin_list[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == -1]), 'Score' = clusters_per_lin_list[[i]]$Score[1], 'Cluster' = 'EGFR')) # Add EGFR row
}

# Reorder so that EGFR dominant lineages are plot first
clusters_per_lin_df <- clusters_per_lin_df[with(clusters_per_lin_df, order(-Score,Num_cells )),]
clusters_per_lin_df$Lineage <- factor(clusters_per_lin_df$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))

# make list object for after second treatment
clusters_per_lin_list_sec <- list()

# Need to get percent values of EGFR for the lineage after the second treatment
for (i in fivecell_cDNA$DabTram){
  currentlin <- filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtramtodabtram')
  Var1 <- c(-1,1)
  Freq <- c(sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtramtodabtram')$Cluster == -1), # EGFR
  sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtramtodabtram')$Cluster == 1))
  clusters_per_lin_list_sec[[i]] <- data.frame('Var1' = Var1, 'Freq' = Freq)
  clusters_per_lin_list_sec[[i]]$Score <- weighted.mean(as.numeric(as.character(clusters_per_lin_list_sec[[i]]$Var1)), clusters_per_lin_list_sec[[i]]$Freq)
}

# Build a dataframe for plotting based on % EGFR or NGFR per lineage after second treatment
clusters_per_lin_df_sec <- data.frame(matrix(ncol = 5, nrow = 0))
colnames(clusters_per_lin_df_sec) <- c('Lineage', 'Percent_cells', 'Num_cells', 'Score', 'Cluster')
for (i in names(clusters_per_lin_list)){

  if (sum(clusters_per_lin_list_sec[[i]]$Freq) < 5){
    clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 1, 'Num_cells' = 0, 'Score' = 0, 'Cluster' = 'Died')) # Add Lineage died row
    clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 0, 'Num_cells' = 0, 'Score' = 0, 'Cluster' = 'NGFR')) # Add NGFR row
    clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 0, 'Num_cells' = 0, 'Score' = 0, 'Cluster' = 'EGFR')) # Add EGFR row
  }else{ # Actually calculate percentages since the lineage survived/is more than 5 cells
      clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 0, 'Num_cells' = 0, 'Score' = clusters_per_lin_list_sec[[i]]$Score[1], 'Cluster' = 'Died')) # Add Lineage died row
      clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == 1]/sum(clusters_per_lin_list_sec[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == 1]), 'Score' = clusters_per_lin_list_sec[[i]]$Score[1], 'Cluster' = 'NGFR')) # Add NGFR row
      clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == -1]/sum(clusters_per_lin_list_sec[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == -1]), 'Score' = clusters_per_lin_list_sec[[i]]$Score[1], 'Cluster' = 'EGFR')) # Add EGFR row
    }
}

# Reorder so that EGFR dominant lineages are plot first
clusters_per_lin_df_sec$Lineage <- factor(clusters_per_lin_df_sec$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))

# Plot
p1 <- ggplot(clusters_per_lin_df, aes(y = Percent_cells, x = Lineage, fill = Cluster)) + geom_bar(stat = 'identity', position = position_fill(reverse = T), color = '#D3D3D3') + scale_fill_manual(values = c("#000000","#FFFFFF")) + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
p2 <- ggplot(clusters_per_lin_df_sec, aes(y = Percent_cells, x = Lineage, fill = Cluster)) + geom_bar(stat = 'identity', position = position_fill(reverse = T), color = '#D3D3D3') + scale_fill_manual(values = c("#FF0000","#000000", '#FFFFFF')) + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/stacked_bar_EGFR_NGFR_Died.pdf')
ggarrange(p1,p2, nrow = 2)
dev.off()
null device 
          1 

Look at whether lineages cluster together in each individual condition - Starting with DabTram

# Get the number of cells in the DabTram resistant lineages in the conditions that got DabTram first
dabtramtococl2_dt_resist_lins <- list()
dabtramtocis_dt_resist_lins <- list()

for (i in fivecell_cDNA$DabTram){
  dabtramtococl2_dt_resist_lins[[i]] <- all_data$Lineage[all_data$Lineage == i & all_data$OG_condition == 'dabtramtococl2']
  dabtramtocis_dt_resist_lins[[i]] <- all_data$Lineage[all_data$Lineage == i & all_data$OG_condition == 'dabtramtocis']
}

# convert list to dataframe
dabtramtococl2_dt_resist_lins_df <- data.frame(num_cells = lengths(dabtramtococl2_dt_resist_lins))
dabtramtococl2_dt_resist_lins_df$Lineage = rownames(dabtramtococl2_dt_resist_lins_df)
dabtramtococl2_dt_resist_lins_df$Lineage <- factor(dabtramtococl2_dt_resist_lins_df$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))

dabtramtocis_dt_resist_lins_df <- data.frame(num_cells = lengths(dabtramtocis_dt_resist_lins))
dabtramtocis_dt_resist_lins_df$Lineage = rownames(dabtramtocis_dt_resist_lins_df)
dabtramtocis_dt_resist_lins_df$Lineage <- factor(dabtramtocis_dt_resist_lins_df$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))

# Plot 
p3 <- ggplot(dabtramtococl2_dt_resist_lins_df, aes(y = log(num_cells+1), x = Lineage)) + geom_col(fill = '#A2248E') + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + labs(title = 'DabTram to CoCl2')

p4 <- ggplot(dabtramtocis_dt_resist_lins_df, aes(y = log(num_cells+1), x = Lineage)) + geom_col(fill = '#9D85BE') + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + labs(title = 'DabTram to Cis') 

pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/stacked_bar_EGFR_NGFR_Died_w_other_second_drugs.pdf', height = 14)
ggarrange(p1+ theme(legend.position = 'bottom'),p2+ theme(legend.position = 'bottom'),p3,p4, nrow = 4)
dev.off()
null device 
          1 

Significance testing of the dabtram simulation

# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtram <- mean(unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtram_sim <- sapply(1:length(dabtram_lin_clust_rand_list), function(y)
  mean(unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtram <- (mean_dabtram-mean(means_dabtram_sim))/sd(means_dabtram_sim)
pval_mean_dabtram <- pnorm(z_mean_dabtram, mean(means_dabtram_sim), sd(means_dabtram_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtram <- weighted.mean(unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtram_sim <- sapply(1:length(dabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtram <- (weighted_mean_dabtram-mean(weighted_means_dabtram_sim))/sd(weighted_means_dabtram_sim)
pval_wmean_dabtram <- pnorm(z_wmean_dabtram, mean(weighted_means_dabtram_sim), sd(weighted_means_dabtram_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtram <- c()
for (i in 1:length(means_dabtram_sim)){
  sim_maxes <- unlist(lapply(dabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_dabtram <- cbind(ttest_pval_dabtram,t.test(x = unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(dabtram_lin_clust_list, dabtram_lin_clust_rand_list,z_mean_dabtram,pval_mean_dabtram, z_wmean_dabtram, ttest_pval_dabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtram_sim_results.RData')
rm(dabtram)

Look at whether lineages cluster together in each individual condition - DabTramtoDabTram

Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtramtodabtram
dabtramtodabtram <- subset(all_data, idents = 'dabtramtodabtram') # Subset down to the dabtramtodabtram object

ElbowPlot(dabtramtodabtram) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
dabtramtodabtram <- FindNeighbors(dabtramtodabtram, dims = 1:10)
dabtramtodabtram <- FindClusters(dabtramtodabtram, resolution = 0.5)
dabtramtodabtram <- RunUMAP(dabtramtodabtram, dims = 1:10)
DimPlot(dabtramtodabtram, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
dabtramtodabtram_lin_clust_list <- list()

for (i in fivecell_cDNA$DabTramtoDabTram){
  temp_df <- as.data.frame(table(Idents(dabtramtodabtram)[dabtramtodabtram$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  dabtramtodabtram_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
dabtramtodabtram_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  dabtramtodabtram_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$DabTramtoDabTram){
    num_cells <- length(dabtramtodabtram$Lineage[dabtramtodabtram$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(dabtramtodabtram), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    dabtramtodabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}

Significance testing of the dabtramtodabtram simulation

# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtramtodabtram <- mean(unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtramtodabtram_sim <- sapply(1:length(dabtramtodabtram_lin_clust_rand_list), function(y)
  mean(unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtramtodabtram <- (mean_dabtramtodabtram-mean(means_dabtramtodabtram_sim))/sd(means_dabtramtodabtram_sim)
pval_mean_dabtramtodabtram <- pnorm(z_mean_dabtramtodabtram, mean(means_dabtramtodabtram_sim), sd(means_dabtramtodabtram_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtramtodabtram <- weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtramtodabtram_sim <- sapply(1:length(dabtramtodabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtramtodabtram <- (weighted_mean_dabtramtodabtram-mean(weighted_means_dabtramtodabtram_sim))/sd(weighted_means_dabtramtodabtram_sim)
pval_wmean_dabtramtodabtram <- pnorm(z_wmean_dabtramtodabtram, mean(weighted_means_dabtramtodabtram_sim), sd(weighted_means_dabtramtodabtram_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtramtodabtram <- c()
for (i in 1:length(means_dabtramtodabtram_sim)){
  sim_maxes <- unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_dabtramtodabtram <- cbind(ttest_pval_dabtramtodabtram,t.test(x = unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(dabtramtodabtram_lin_clust_list, dabtramtodabtram_lin_clust_rand_list,z_mean_dabtramtodabtram,pval_mean_dabtramtodabtram, z_wmean_dabtramtodabtram, ttest_pval_dabtramtodabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtodabtram_sim_results.RData')
rm(dabtramtodabtram)

Look at whether lineages cluster together in each individual condition - DabTramtoCis

Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtramtocis
dabtramtocis <- subset(all_data, idents = 'dabtramtocis') # Subset down to the dabtramtocis object

ElbowPlot(dabtramtocis) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
dabtramtocis <- FindNeighbors(dabtramtocis, dims = 1:10)
dabtramtocis <- FindClusters(dabtramtocis, resolution = 0.5)
dabtramtocis <- RunUMAP(dabtramtocis, dims = 1:10)
DimPlot(dabtramtocis, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
dabtramtocis_lin_clust_list <- list()

for (i in fivecell_cDNA$DabTramtoCis){
  temp_df <- as.data.frame(table(Idents(dabtramtocis)[dabtramtocis$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  dabtramtocis_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
dabtramtocis_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  dabtramtocis_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$DabTramtoCis){
    num_cells <- length(dabtramtocis$Lineage[dabtramtocis$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(dabtramtocis), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    dabtramtocis_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}

Significance testing of the dabtramtocis simulation

# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtramtocis <- mean(unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtramtocis_sim <- sapply(1:length(dabtramtocis_lin_clust_rand_list), function(y)
  mean(unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtramtocis <- (mean_dabtramtocis-mean(means_dabtramtocis_sim))/sd(means_dabtramtocis_sim)
pval_mean_dabtramtocis <- pnorm(z_mean_dabtramtocis, mean(means_dabtramtocis_sim), sd(means_dabtramtocis_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtramtocis <- weighted.mean(unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtocis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtramtocis_sim <- sapply(1:length(dabtramtocis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtramtocis <- (weighted_mean_dabtramtocis-mean(weighted_means_dabtramtocis_sim))/sd(weighted_means_dabtramtocis_sim)
pval_wmean_dabtramtocis <- pnorm(z_wmean_dabtramtocis, mean(weighted_means_dabtramtocis_sim), sd(weighted_means_dabtramtocis_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtramtocis <- c()
for (i in 1:length(means_dabtramtocis_sim)){
  sim_maxes <- unlist(lapply(dabtramtocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_dabtramtocis <- cbind(ttest_pval_dabtramtocis,t.test(x = unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtramtocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(dabtramtocis_lin_clust_list, dabtramtocis_lin_clust_rand_list,z_mean_dabtramtocis,pval_mean_dabtramtocis, z_wmean_dabtramtocis, ttest_pval_dabtramtocis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtocis_sim_results.RData')
rm(dabtramtocis)

Look at whether lineages cluster together in each individual condition - DabTramtoCis

Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtramtococl2
dabtramtococl2 <- subset(all_data, idents = 'dabtramtococl2') # Subset down to the dabtramtococl2 object

ElbowPlot(dabtramtococl2) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
dabtramtococl2 <- FindNeighbors(dabtramtococl2, dims = 1:10)
dabtramtococl2 <- FindClusters(dabtramtococl2, resolution = 0.5)
dabtramtococl2 <- RunUMAP(dabtramtococl2, dims = 1:10)
DimPlot(dabtramtococl2, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
dabtramtococl2_lin_clust_list <- list()

for (i in fivecell_cDNA$DabTramtoCoCl2){
  temp_df <- as.data.frame(table(Idents(dabtramtococl2)[dabtramtococl2$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  dabtramtococl2_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
dabtramtococl2_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  dabtramtococl2_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$DabTramtoCoCl2){
    num_cells <- length(dabtramtococl2$Lineage[dabtramtococl2$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(dabtramtococl2), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    dabtramtococl2_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}

Significance testing of the dabtramtococl2 simulation

# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtramtococl2 <- mean(unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtramtococl2_sim <- sapply(1:length(dabtramtococl2_lin_clust_rand_list), function(y)
  mean(unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtramtococl2 <- (mean_dabtramtococl2-mean(means_dabtramtococl2_sim))/sd(means_dabtramtococl2_sim)
pval_mean_dabtramtococl2 <- pnorm(z_mean_dabtramtococl2, mean(means_dabtramtococl2_sim), sd(means_dabtramtococl2_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtramtococl2 <- weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtococl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtramtococl2_sim <- sapply(1:length(dabtramtococl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtramtococl2 <- (weighted_mean_dabtramtococl2-mean(weighted_means_dabtramtococl2_sim))/sd(weighted_means_dabtramtococl2_sim)
pval_wmean_dabtramtococl2 <- pnorm(z_wmean_dabtramtococl2, mean(weighted_means_dabtramtococl2_sim), sd(weighted_means_dabtramtococl2_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtramtococl2 <- c()
for (i in 1:length(means_dabtramtococl2_sim)){
  sim_maxes <- unlist(lapply(dabtramtococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_dabtramtococl2 <- cbind(ttest_pval_dabtramtococl2,t.test(x = unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtramtococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(dabtramtococl2_lin_clust_list, dabtramtococl2_lin_clust_rand_list,z_mean_dabtramtococl2,pval_mean_dabtramtococl2, z_wmean_dabtramtococl2, ttest_pval_dabtramtococl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtococl2_sim_results.RData')
rm(dabtramtococl2)

Look at whether lineages cluster together in each individual condition - Starting with DabTram

Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2
cocl2 <- subset(all_data, idents = 'cocl2') # Subset down to the cocl2 object

ElbowPlot(cocl2) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cocl2 <- FindNeighbors(cocl2, dims = 1:10)
cocl2 <- FindClusters(cocl2, resolution = 0.5)
cocl2 <- RunUMAP(cocl2, dims = 1:10)
DimPlot(cocl2, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cocl2_lin_clust_list <- list()

for (i in fivecell_cDNA$CoCl2){
  temp_df <- as.data.frame(table(Idents(cocl2)[cocl2$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cocl2_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cocl2_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cocl2_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CoCl2){
    num_cells <- length(cocl2$Lineage[cocl2$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cocl2), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cocl2_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}

Significance testing of the cocl2 simulation

# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2 <- mean(unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2_sim <- sapply(1:length(cocl2_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2 <- (mean_cocl2-mean(means_cocl2_sim))/sd(means_cocl2_sim)
pval_mean_cocl2 <- pnorm(z_mean_cocl2, mean(means_cocl2_sim), sd(means_cocl2_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2 <- weighted.mean(unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2_sim <- sapply(1:length(cocl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2 <- (weighted_mean_cocl2-mean(weighted_means_cocl2_sim))/sd(weighted_means_cocl2_sim)
pval_wmean_cocl2 <- pnorm(z_wmean_cocl2, mean(weighted_means_cocl2_sim), sd(weighted_means_cocl2_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2 <- c()
for (i in 1:length(means_cocl2_sim)){
  sim_maxes <- unlist(lapply(cocl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cocl2 <- cbind(ttest_pval_cocl2,t.test(x = unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cocl2_lin_clust_list, cocl2_lin_clust_rand_list,z_mean_cocl2,pval_mean_cocl2, z_wmean_cocl2, ttest_pval_cocl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2_sim_results.RData')
rm(cocl2)

Look at whether lineages cluster together in each individual condition - Starting with CoCl2toCoCl2

Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2tococl2
cocl2tococl2 <- subset(all_data, idents = 'cocl2tococl2') # Subset down to the cocl2tococl2 object

ElbowPlot(cocl2tococl2) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cocl2tococl2 <- FindNeighbors(cocl2tococl2, dims = 1:10)
cocl2tococl2 <- FindClusters(cocl2tococl2, resolution = 0.5)
cocl2tococl2 <- RunUMAP(cocl2tococl2, dims = 1:10)
DimPlot(cocl2tococl2, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cocl2tococl2_lin_clust_list <- list()

for (i in fivecell_cDNA$CoCl2toCoCl2){
  temp_df <- as.data.frame(table(Idents(cocl2tococl2)[cocl2tococl2$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cocl2tococl2_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cocl2tococl2_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cocl2tococl2_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CoCl2toCoCl2){
    num_cells <- length(cocl2tococl2$Lineage[cocl2tococl2$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cocl2tococl2), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cocl2tococl2_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}

Significance testing of the cocl2tococl2 simulation

# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2tococl2 <- mean(unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2tococl2_sim <- sapply(1:length(cocl2tococl2_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2tococl2 <- (mean_cocl2tococl2-mean(means_cocl2tococl2_sim))/sd(means_cocl2tococl2_sim)
pval_mean_cocl2tococl2 <- pnorm(z_mean_cocl2tococl2, mean(means_cocl2tococl2_sim), sd(means_cocl2tococl2_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2tococl2 <- weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tococl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2tococl2_sim <- sapply(1:length(cocl2tococl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2tococl2 <- (weighted_mean_cocl2tococl2-mean(weighted_means_cocl2tococl2_sim))/sd(weighted_means_cocl2tococl2_sim)
pval_wmean_cocl2tococl2 <- pnorm(z_wmean_cocl2tococl2, mean(weighted_means_cocl2tococl2_sim), sd(weighted_means_cocl2tococl2_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2tococl2 <- c()
for (i in 1:length(means_cocl2tococl2_sim)){
  sim_maxes <- unlist(lapply(cocl2tococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cocl2tococl2 <- cbind(ttest_pval_cocl2tococl2,t.test(x = unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2tococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cocl2tococl2_lin_clust_list, cocl2tococl2_lin_clust_rand_list,z_mean_cocl2tococl2,pval_mean_cocl2tococl2, z_wmean_cocl2tococl2, ttest_pval_cocl2tococl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tococl2_sim_results.RData')
rm(cocl2tococl2)

Look at whether lineages cluster together in each individual condition - Starting with CoCl2toCis

Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2tocis
cocl2tocis <- subset(all_data, idents = 'cocl2tocis') # Subset down to the cocl2tocis object

ElbowPlot(cocl2tocis) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cocl2tocis <- FindNeighbors(cocl2tocis, dims = 1:10)
cocl2tocis <- FindClusters(cocl2tocis, resolution = 0.5)
cocl2tocis <- RunUMAP(cocl2tocis, dims = 1:10)
DimPlot(cocl2tocis, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cocl2tocis_lin_clust_list <- list()

for (i in fivecell_cDNA$CoCl2toCis){
  temp_df <- as.data.frame(table(Idents(cocl2tocis)[cocl2tocis$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cocl2tocis_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cocl2tocis_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cocl2tocis_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CoCl2toCis){
    num_cells <- length(cocl2tocis$Lineage[cocl2tocis$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cocl2tocis), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cocl2tocis_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}

Significance testing of the cocl2tocis simulation

# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2tocis <- mean(unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2tocis_sim <- sapply(1:length(cocl2tocis_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2tocis <- (mean_cocl2tocis-mean(means_cocl2tocis_sim))/sd(means_cocl2tocis_sim)
pval_mean_cocl2tocis <- pnorm(z_mean_cocl2tocis, mean(means_cocl2tocis_sim), sd(means_cocl2tocis_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2tocis <- weighted.mean(unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tocis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2tocis_sim <- sapply(1:length(cocl2tocis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2tocis <- (weighted_mean_cocl2tocis-mean(weighted_means_cocl2tocis_sim))/sd(weighted_means_cocl2tocis_sim)
pval_wmean_cocl2tocis <- pnorm(z_wmean_cocl2tocis, mean(weighted_means_cocl2tocis_sim), sd(weighted_means_cocl2tocis_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2tocis <- c()
for (i in 1:length(means_cocl2tocis_sim)){
  sim_maxes <- unlist(lapply(cocl2tocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cocl2tocis <- cbind(ttest_pval_cocl2tocis,t.test(x = unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2tocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cocl2tocis_lin_clust_list, cocl2tocis_lin_clust_rand_list,z_mean_cocl2tocis,pval_mean_cocl2tocis, z_wmean_cocl2tocis, ttest_pval_cocl2tocis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tocis_sim_results.RData')
rm(cocl2tocis)

Look at whether lineages cluster together in each individual condition - Starting with CoCl2toDabTram

Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2todabtram
cocl2todabtram <- subset(all_data, idents = 'cocl2todabtram') # Subset down to the cocl2todabtram object

ElbowPlot(cocl2todabtram) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cocl2todabtram <- FindNeighbors(cocl2todabtram, dims = 1:10)
cocl2todabtram <- FindClusters(cocl2todabtram, resolution = 0.5)
cocl2todabtram <- RunUMAP(cocl2todabtram, dims = 1:10)
DimPlot(cocl2todabtram, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cocl2todabtram_lin_clust_list <- list()

for (i in fivecell_cDNA$CoCl2toDabTram){
  temp_df <- as.data.frame(table(Idents(cocl2todabtram)[cocl2todabtram$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cocl2todabtram_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cocl2todabtram_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cocl2todabtram_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CoCl2toDabTram){
    num_cells <- length(cocl2todabtram$Lineage[cocl2todabtram$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cocl2todabtram), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cocl2todabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}

Significance testing of the cocl2todabtram simulation

# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2todabtram <- mean(unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2todabtram_sim <- sapply(1:length(cocl2todabtram_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2todabtram <- (mean_cocl2todabtram-mean(means_cocl2todabtram_sim))/sd(means_cocl2todabtram_sim)
pval_mean_cocl2todabtram <- pnorm(z_mean_cocl2todabtram, mean(means_cocl2todabtram_sim), sd(means_cocl2todabtram_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2todabtram <- weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2todabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2todabtram_sim <- sapply(1:length(cocl2todabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2todabtram <- (weighted_mean_cocl2todabtram-mean(weighted_means_cocl2todabtram_sim))/sd(weighted_means_cocl2todabtram_sim)
pval_wmean_cocl2todabtram <- pnorm(z_wmean_cocl2todabtram, mean(weighted_means_cocl2todabtram_sim), sd(weighted_means_cocl2todabtram_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2todabtram <- c()
for (i in 1:length(means_cocl2todabtram_sim)){
  sim_maxes <- unlist(lapply(cocl2todabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cocl2todabtram <- cbind(ttest_pval_cocl2todabtram,t.test(x = unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2todabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cocl2todabtram_lin_clust_list, cocl2todabtram_lin_clust_rand_list,z_mean_cocl2todabtram,pval_mean_cocl2todabtram, z_wmean_cocl2todabtram, ttest_pval_cocl2todabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2todabtram_sim_results.RData')
rm(cocl2todabtram)

Look at whether lineages cluster together in each individual condition - Starting with Cis

Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cis
cis <- subset(all_data, idents = 'cis') # Subset down to the cis object

ElbowPlot(cis) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cis <- FindNeighbors(cis, dims = 1:10)
cis <- FindClusters(cis, resolution = 0.5)
cis <- RunUMAP(cis, dims = 1:10)
DimPlot(cis, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cis_lin_clust_list <- list()

for (i in fivecell_cDNA$Cis){
  temp_df <- as.data.frame(table(Idents(cis)[cis$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cis_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cis_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cis_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$Cis){
    num_cells <- length(cis$Lineage[cis$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cis), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cis_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}

Significance testing of the cis simulation

# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cis <- mean(unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cis_sim <- sapply(1:length(cis_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cis <- (mean_cis-mean(means_cis_sim))/sd(means_cis_sim)
pval_mean_cis <- pnorm(z_mean_cis, mean(means_cis_sim), sd(means_cis_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cis <- weighted.mean(unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cis_sim <- sapply(1:length(cis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cis <- (weighted_mean_cis-mean(weighted_means_cis_sim))/sd(weighted_means_cis_sim)
pval_wmean_cis <- pnorm(z_wmean_cis, mean(weighted_means_cis_sim), sd(weighted_means_cis_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cis <- c()
for (i in 1:length(means_cis_sim)){
  sim_maxes <- unlist(lapply(cis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cis <- cbind(ttest_pval_cis,t.test(x = unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cis_lin_clust_list, cis_lin_clust_rand_list,z_mean_cis,pval_mean_cis, z_wmean_cis, ttest_pval_cis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cis_sim_results.RData')
rm(cis)

Look at whether lineages cluster together in each individual condition - Starting with cistocis

Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cistocis
cistocis <- subset(all_data, idents = 'cistocis') # Subset down to the cistocis object

ElbowPlot(cistocis) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cistocis <- FindNeighbors(cistocis, dims = 1:10)
cistocis <- FindClusters(cistocis, resolution = 0.5)
cistocis <- RunUMAP(cistocis, dims = 1:10)
DimPlot(cistocis, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cistocis_lin_clust_list <- list()

for (i in fivecell_cDNA$CistoCis){
  temp_df <- as.data.frame(table(Idents(cistocis)[cistocis$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cistocis_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cistocis_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cistocis_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CistoCis){
    num_cells <- length(cistocis$Lineage[cistocis$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cistocis), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cistocis_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}

Significance testing of the cistocis simulation

# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cistocis <- mean(unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cistocis_sim <- sapply(1:length(cistocis_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cistocis <- (mean_cistocis-mean(means_cistocis_sim))/sd(means_cistocis_sim)
pval_mean_cistocis <- pnorm(z_mean_cistocis, mean(means_cistocis_sim), sd(means_cistocis_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cistocis <- weighted.mean(unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistocis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cistocis_sim <- sapply(1:length(cistocis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cistocis <- (weighted_mean_cistocis-mean(weighted_means_cistocis_sim))/sd(weighted_means_cistocis_sim)
pval_wmean_cistocis <- pnorm(z_wmean_cistocis, mean(weighted_means_cistocis_sim), sd(weighted_means_cistocis_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cistocis <- c()
for (i in 1:length(means_cistocis_sim)){
  sim_maxes <- unlist(lapply(cistocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cistocis <- cbind(ttest_pval_cistocis,t.test(x = unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cistocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cistocis_lin_clust_list, cistocis_lin_clust_rand_list,z_mean_cistocis,pval_mean_cistocis, z_wmean_cistocis, ttest_pval_cistocis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistocis_sim_results.RData')
rm(cistocis)

Look at whether lineages cluster together in each individual condition - Starting with cistodabtram

Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cistodabtram
cistodabtram <- subset(all_data, idents = 'cistodabtram') # Subset down to the cistodabtram object

ElbowPlot(cistodabtram) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cistodabtram <- FindNeighbors(cistodabtram, dims = 1:10)
cistodabtram <- FindClusters(cistodabtram, resolution = 0.5)
cistodabtram <- RunUMAP(cistodabtram, dims = 1:10)
DimPlot(cistodabtram, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cistodabtram_lin_clust_list <- list()

for (i in fivecell_cDNA$CistoDabTram){
  temp_df <- as.data.frame(table(Idents(cistodabtram)[cistodabtram$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cistodabtram_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cistodabtram_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cistodabtram_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CistoDabTram){
    num_cells <- length(cistodabtram$Lineage[cistodabtram$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cistodabtram), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cistodabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}

Significance testing of the cistodabtram simulation

# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cistodabtram <- mean(unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cistodabtram_sim <- sapply(1:length(cistodabtram_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cistodabtram <- (mean_cistodabtram-mean(means_cistodabtram_sim))/sd(means_cistodabtram_sim)
pval_mean_cistodabtram <- pnorm(z_mean_cistodabtram, mean(means_cistodabtram_sim), sd(means_cistodabtram_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cistodabtram <- weighted.mean(unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cistodabtram_sim <- sapply(1:length(cistodabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cistodabtram <- (weighted_mean_cistodabtram-mean(weighted_means_cistodabtram_sim))/sd(weighted_means_cistodabtram_sim)
pval_wmean_cistodabtram <- pnorm(z_wmean_cistodabtram, mean(weighted_means_cistodabtram_sim), sd(weighted_means_cistodabtram_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cistodabtram <- c()
for (i in 1:length(means_cistodabtram_sim)){
  sim_maxes <- unlist(lapply(cistodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cistodabtram <- cbind(ttest_pval_cistodabtram,t.test(x = unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cistodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cistodabtram_lin_clust_list, cistodabtram_lin_clust_rand_list,z_mean_cistodabtram,pval_mean_cistodabtram, z_wmean_cistodabtram, ttest_pval_cistodabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistodabtram_sim_results.RData')
rm(cistodabtram)

Look at whether lineages cluster together in each individual condition - Starting with cistococl2

Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cistococl2
cistococl2 <- subset(all_data, idents = 'cistococl2') # Subset down to the cistococl2 object

ElbowPlot(cistococl2) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cistococl2 <- FindNeighbors(cistococl2, dims = 1:10)
cistococl2 <- FindClusters(cistococl2, resolution = 0.5)
cistococl2 <- RunUMAP(cistococl2, dims = 1:10)
DimPlot(cistococl2, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cistococl2_lin_clust_list <- list()

for (i in fivecell_cDNA$CistoCoCl2){
  temp_df <- as.data.frame(table(Idents(cistococl2)[cistococl2$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cistococl2_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cistococl2_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cistococl2_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CistoCoCl2){
    num_cells <- length(cistococl2$Lineage[cistococl2$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cistococl2), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cistococl2_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}

Significance testing of the cistococl2 simulation

# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cistococl2 <- mean(unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cistococl2_sim <- sapply(1:length(cistococl2_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cistococl2 <- (mean_cistococl2-mean(means_cistococl2_sim))/sd(means_cistococl2_sim)
pval_mean_cistococl2 <- pnorm(z_mean_cistococl2, mean(means_cistococl2_sim), sd(means_cistococl2_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cistococl2 <- weighted.mean(unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistococl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cistococl2_sim <- sapply(1:length(cistococl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cistococl2 <- (weighted_mean_cistococl2-mean(weighted_means_cistococl2_sim))/sd(weighted_means_cistococl2_sim)
pval_wmean_cistococl2 <- pnorm(z_wmean_cistococl2, mean(weighted_means_cistococl2_sim), sd(weighted_means_cistococl2_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cistococl2 <- c()
for (i in 1:length(means_cistococl2_sim)){
  sim_maxes <- unlist(lapply(cistococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cistococl2 <- cbind(ttest_pval_cistococl2,t.test(x = unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cistococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cistococl2_lin_clust_list, cistococl2_lin_clust_rand_list,z_mean_cistococl2,pval_mean_cistococl2, z_wmean_cistococl2, ttest_pval_cistococl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistococl2_sim_results.RData')
rm(cistococl2)

Build dataframe of overall simulation results

sim_results <- rbind(
  dabtram <- data.frame("Mean_Zscore" = z_mean_dabtram, "Mean_pval" = pval_mean_dabtram, "Weighted_Mean_Zscore" = z_wmean_dabtram, "Weighted_Mean_pval" = pval_wmean_dabtram, ttest_sims_sig = sum(ttest_pval_dabtram <= 0.05)),
  dabtramtodabtram <- data.frame("Mean_Zscore" = z_mean_dabtramtodabtram, "Mean_pval" = pval_mean_dabtramtodabtram, "Weighted_Mean_Zscore" = z_wmean_dabtramtodabtram, "Weighted_Mean_pval" = pval_wmean_dabtramtodabtram, ttest_sims_sig = sum(ttest_pval_dabtramtodabtram <= 0.05)),
  dabtramtococl2 <- data.frame("Mean_Zscore" = z_mean_dabtramtococl2, "Mean_pval" = pval_mean_dabtramtococl2, "Weighted_Mean_Zscore" = z_wmean_dabtramtococl2, "Weighted_Mean_pval" = pval_wmean_dabtramtococl2, ttest_sims_sig = sum(ttest_pval_dabtramtococl2 <= 0.05)),
  dabtramtocis <- data.frame("Mean_Zscore" = z_mean_dabtramtocis, "Mean_pval" = pval_mean_dabtramtocis, "Weighted_Mean_Zscore" = z_wmean_dabtramtocis, "Weighted_Mean_pval" = pval_wmean_dabtramtocis, ttest_sims_sig = sum(ttest_pval_dabtramtocis <= 0.05)),
  cis <- data.frame("Mean_Zscore" = z_mean_cis, "Mean_pval" = pval_mean_cis, "Weighted_Mean_Zscore" = z_wmean_cis, "Weighted_Mean_pval" = pval_wmean_cis, ttest_sims_sig = sum(ttest_pval_cis <= 0.05)),
  cistodabtram <- data.frame("Mean_Zscore" = z_mean_cistodabtram, "Mean_pval" = pval_mean_cistodabtram, "Weighted_Mean_Zscore" = z_wmean_cistodabtram, "Weighted_Mean_pval" = pval_wmean_cistodabtram, ttest_sims_sig = sum(ttest_pval_cistodabtram <= 0.05)),
  cistococl2 <- data.frame("Mean_Zscore" = z_mean_cistococl2, "Mean_pval" = pval_mean_cistococl2, "Weighted_Mean_Zscore" = z_wmean_cistococl2, "Weighted_Mean_pval" = pval_wmean_cistococl2, ttest_sims_sig = sum(ttest_pval_cistococl2 <= 0.05)),
  cistocis <- data.frame("Mean_Zscore" = z_mean_cistocis, "Mean_pval" = pval_mean_cistocis, "Weighted_Mean_Zscore" = z_wmean_cistocis, "Weighted_Mean_pval" = pval_wmean_cistocis, ttest_sims_sig = sum(ttest_pval_cistocis <= 0.05)),
  cocl2 <- data.frame("Mean_Zscore" = z_mean_cocl2, "Mean_pval" = pval_mean_cocl2, "Weighted_Mean_Zscore" = z_wmean_cocl2, "Weighted_Mean_pval" = pval_wmean_cocl2, ttest_sims_sig = sum(ttest_pval_cocl2 <= 0.05)),
  cocl2todabtram <- data.frame("Mean_Zscore" = z_mean_cocl2todabtram, "Mean_pval" = pval_mean_cocl2todabtram, "Weighted_Mean_Zscore" = z_wmean_cocl2todabtram, "Weighted_Mean_pval" = pval_wmean_cocl2todabtram, ttest_sims_sig = sum(ttest_pval_cocl2todabtram <= 0.05)),
  cocl2tococl2 <- data.frame("Mean_Zscore" = z_mean_cocl2tococl2, "Mean_pval" = pval_mean_cocl2tococl2, "Weighted_Mean_Zscore" = z_wmean_cocl2tococl2, "Weighted_Mean_pval" = pval_wmean_cocl2tococl2, ttest_sims_sig = sum(ttest_pval_cocl2tococl2 <= 0.05)),
  cocl2tocis <- data.frame("Mean_Zscore" = z_mean_cocl2tocis, "Mean_pval" = pval_mean_cocl2tocis, "Weighted_Mean_Zscore" = z_wmean_cocl2tocis, "Weighted_Mean_pval" = pval_wmean_cocl2tocis, ttest_sims_sig = sum(ttest_pval_cocl2tocis <= 0.05))
)

rownames(sim_results) <- c("dabtram", "dabtramtodabtram", "dabtramtococl2", "dabtramtocis", "cis", "cistodabtram", "cistococl2", "cistocis", "cocl2", "cocl2todabtram", "cocl2tococl2", "cocl2tocis")

write.csv(sim_results, '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/sim_results.csv', row.names = T) 

Load simulation data back in

sim_results <- read.csv('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/sim_results.csv')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtram_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtodabtram_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtococl2_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtocis_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2todabtram_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tococl2_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tocis_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cis_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistodabtram_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistococl2_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistocis_sim_results.RData')

Plot heatmaps of true percentage of cells per lineage in each cluster vs random simulations


breaks <- seq(0, 1, 0.1)

# DabTram
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/proportion_clusters_heatmaps.pdf', width = 16)
dabtram_pcnts <- as.data.frame(sapply(dabtram_lin_clust_list, '[[', 3))
cell_nums <- sapply(dabtram_lin_clust_list, function(x) sum(x$Num_cells))
rownames(dabtram_pcnts) <- c("0","1","2","3","4","5","6")
colnames(dabtram_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(dabtram_pcnts,cluster_rows = F, color = inferno(11), main = "DabTram", breaks = breaks) # 
p

dabtram_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(dabtram_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(dabtram_lin_clust_rand_list))
rownames(dabtram_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(dabtram_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
dabtram_pcnts_rand <- dabtram_pcnts_rand[,p$tree_col$order]
pheatmap(dabtram_pcnts_rand,cluster_rows = F, color = inferno(11), main = "DabTram Rand", breaks = breaks, cluster_cols = F)

# DabTram to DabTram
dabtramtodabtram_pcnts <- as.data.frame(sapply(dabtramtodabtram_lin_clust_list, '[[', 3))
cell_nums <- sapply(dabtramtodabtram_lin_clust_list, function(x) sum(x$Num_cells))
rownames(dabtramtodabtram_pcnts) <- c("0","1","2","3","4","5","6")
colnames(dabtramtodabtram_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(dabtramtodabtram_pcnts,cluster_rows = F, color = inferno(11), main = "DabTram to DabTram", breaks = breaks)
p

dabtramtodabtram_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(dabtramtodabtram_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(dabtramtodabtram_lin_clust_rand_list))
rownames(dabtramtodabtram_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(dabtramtodabtram_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
dabtramtodabtram_pcnts_rand <- dabtramtodabtram_pcnts_rand[,p$tree_col$order]
pheatmap(dabtramtodabtram_pcnts_rand,cluster_rows = F, color = inferno(11), main = "DabTram to DabTram Rand", breaks = breaks, cluster_cols = F)

# DabTram to CoCl2
dabtramtococl2_pcnts <- as.data.frame(sapply(dabtramtococl2_lin_clust_list, '[[', 3))
cell_nums <- sapply(dabtramtococl2_lin_clust_list, function(x) sum(x$Num_cells))
rownames(dabtramtococl2_pcnts) <- c("0","1","2","3","4","5","6","7")
colnames(dabtramtococl2_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(dabtramtococl2_pcnts,cluster_rows = F, color = inferno(11), main = "DabTram to CoCl2", breaks = breaks)
p

dabtramtococl2_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(dabtramtococl2_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(dabtramtococl2_lin_clust_rand_list))
rownames(dabtramtococl2_pcnts_rand) <- c("0","1","2","3","4","5","6","7")
colnames(dabtramtococl2_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
dabtramtococl2_pcnts_rand <- dabtramtococl2_pcnts_rand[,p$tree_col$order]
pheatmap(dabtramtococl2_pcnts_rand,cluster_rows = F, color = inferno(11), main = "DabTram to CoCl2 Rand", breaks = breaks, cluster_cols = F)

# DabTram to Cis
dabtramtocis_pcnts <- as.data.frame(sapply(dabtramtocis_lin_clust_list, '[[', 3))
cell_nums <- sapply(dabtramtocis_lin_clust_list, function(x) sum(x$Num_cells))
rownames(dabtramtocis_pcnts) <- c("0","1","2","3","4","5","6")
colnames(dabtramtocis_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(dabtramtocis_pcnts,cluster_rows = F, color = inferno(11), main = "DabTram to Cis", breaks = breaks)
p

dabtramtocis_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(dabtramtocis_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(dabtramtocis_lin_clust_rand_list))
rownames(dabtramtocis_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(dabtramtocis_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
dabtramtocis_pcnts_rand <- dabtramtocis_pcnts_rand[,p$tree_col$order]
pheatmap(dabtramtocis_pcnts_rand,cluster_rows = F, color = inferno(11), main = "DabTram to Cis Rand", breaks = breaks, cluster_cols = F)

# CoCl2 objects
cocl2_pcnts <- as.data.frame(sapply(cocl2_lin_clust_list, '[[', 3))
cell_nums <- sapply(cocl2_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cocl2_pcnts) <- c("0","1","2","3","4","5","6","7","8")
colnames(cocl2_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cocl2_pcnts,cluster_rows = F, color = inferno(11), main = "CoCl2", breaks = breaks)
p

cocl2_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cocl2_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cocl2_lin_clust_rand_list))
rownames(cocl2_pcnts_rand) <- c("0","1","2","3","4","5","6","7","8")
colnames(cocl2_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cocl2_pcnts_rand <- cocl2_pcnts_rand[,p$tree_col$order]
pheatmap(cocl2_pcnts_rand,cluster_rows = F, color = inferno(11), main = "CoCl2 Rand", breaks = breaks, cluster_cols = F)

# CoCl2 to DabTram
cocl2todabtram_pcnts <- as.data.frame(sapply(cocl2todabtram_lin_clust_list, '[[', 3))
cell_nums <- sapply(cocl2todabtram_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cocl2todabtram_pcnts) <- c("0","1","2","3","4","5","6")
colnames(cocl2todabtram_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cocl2todabtram_pcnts,cluster_rows = F, color = inferno(11), main = "CoCl2 to DabTram", breaks = breaks)
p

cocl2todabtram_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cocl2todabtram_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cocl2todabtram_lin_clust_rand_list))
rownames(cocl2todabtram_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(cocl2todabtram_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cocl2todabtram_pcnts_rand <- cocl2todabtram_pcnts_rand[,p$tree_col$order]
pheatmap(cocl2todabtram_pcnts_rand,cluster_rows = F, color = inferno(11), main = "CoCl2 to DabTram Rand", breaks = breaks, cluster_cols = F)

# CoCl2 to CoCl2
cocl2tococl2_pcnts <- as.data.frame(sapply(cocl2tococl2_lin_clust_list, '[[', 3))
cell_nums <- sapply(cocl2tococl2_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cocl2tococl2_pcnts) <- c("0","1","2","3","4","5")
colnames(cocl2tococl2_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cocl2tococl2_pcnts,cluster_rows = F, color = inferno(11), main = "CoCl2 to CoCl2", breaks = breaks)
p

cocl2tococl2_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cocl2tococl2_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cocl2tococl2_lin_clust_rand_list))
rownames(cocl2tococl2_pcnts_rand) <- c("0","1","2","3","4","5")
colnames(cocl2tococl2_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cocl2tococl2_pcnts_rand <- cocl2tococl2_pcnts_rand[,p$tree_col$order]
pheatmap(cocl2tococl2_pcnts_rand,cluster_rows = F, color = inferno(11), main = "CoCl2 to CoCl2 Rand", breaks = breaks, cluster_cols = F)

# CoCl2 to Cis
cocl2tocis_pcnts <- as.data.frame(sapply(cocl2tocis_lin_clust_list, '[[', 3))
cell_nums <- sapply(cocl2tocis_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cocl2tocis_pcnts) <- c("0","1","2","3","4","5")
colnames(cocl2tocis_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cocl2tocis_pcnts,cluster_rows = F, color = inferno(11), main = "CoCl2 to Cis", breaks = breaks)
p

cocl2tocis_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cocl2tocis_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cocl2tocis_lin_clust_rand_list))
rownames(cocl2tocis_pcnts_rand) <- c("0","1","2","3","4","5")
colnames(cocl2tocis_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cocl2tocis_pcnts_rand <- cocl2tocis_pcnts_rand[,p$tree_col$order]
pheatmap(cocl2tocis_pcnts_rand,cluster_rows = F, color = inferno(11), main = "CoCl2 to Cis Rand", breaks = breaks, cluster_cols = F)

# Cis 
cis_pcnts <- as.data.frame(sapply(cis_lin_clust_list, '[[', 3))
cell_nums <- sapply(cis_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cis_pcnts) <- c("0","1","2","3","4","5","6","7")
colnames(cis_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cis_pcnts,cluster_rows = F, color = inferno(10), main = "Cis", breaks = breaks)
p

cis_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cis_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cis_lin_clust_rand_list))
rownames(cis_pcnts_rand) <- c("0","1","2","3","4","5","6","7")
colnames(cis_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cis_pcnts_rand <- cis_pcnts_rand[,p$tree_col$order]
pheatmap(cis_pcnts_rand,cluster_rows = F, color = inferno(11), main = "Cis Rand", breaks = breaks, cluster_cols = F)

# Cis to DabTram
cistodabtram_pcnts <- as.data.frame(sapply(cistodabtram_lin_clust_list, '[[', 3))
cell_nums <- sapply(cistodabtram_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cistodabtram_pcnts) <- c("0","1","2","3","4","5","6","7")
colnames(cistodabtram_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cistodabtram_pcnts,cluster_rows = F, color = inferno(11), main = "Cis to DabTram", breaks = breaks)
p

cistodabtram_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cistodabtram_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cistodabtram_lin_clust_rand_list))
rownames(cistodabtram_pcnts_rand) <- c("0","1","2","3","4","5","6","7")
colnames(cistodabtram_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cistodabtram_pcnts_rand <- cistodabtram_pcnts_rand[,p$tree_col$order]
pheatmap(cistodabtram_pcnts_rand,cluster_rows = F, color = inferno(11), main = "Cis to DabTram Rand", breaks = breaks, cluster_cols = F)

# Cis to CoCl2
cistococl2_pcnts <- as.data.frame(sapply(cistococl2_lin_clust_list, '[[', 3))
cell_nums <- sapply(cistococl2_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cistococl2_pcnts) <- c("0","1","2","3","4","5","6")
colnames(cistococl2_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cistococl2_pcnts,cluster_rows = F, color = inferno(11), main = "Cis to CoCl2", breaks = breaks)
p

cistococl2_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cistococl2_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cistococl2_lin_clust_rand_list))
rownames(cistococl2_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(cistococl2_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cistococl2_pcnts_rand <- cistococl2_pcnts_rand[,p$tree_col$order]
pheatmap(cistococl2_pcnts_rand,cluster_rows = F, color = inferno(11), main = "Cis to CoCl2 Rand", breaks = breaks, cluster_cols = F)

# Cis to Cis
cistocis_pcnts <- as.data.frame(sapply(cistocis_lin_clust_list, '[[', 3))
cell_nums <- sapply(cistocis_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cistocis_pcnts) <- c("0","1","2","3","4","5","6","7","8")
colnames(cistocis_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cistocis_pcnts,cluster_rows = F, color = inferno(11), main = "Cis to Cis", breaks = breaks)
p

cistocis_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cistocis_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cistocis_lin_clust_rand_list))
rownames(cistocis_pcnts_rand) <- c("0","1","2","3","4","5","6","7","8")
colnames(cistocis_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cistocis_pcnts_rand <- cistocis_pcnts_rand[,p$tree_col$order]
pheatmap(cistocis_pcnts_rand,cluster_rows = F, color = inferno(11), main = "Cis to Cis Rand", breaks = breaks, cluster_cols = F)
dev.off()

plot the different test statistics - histogram of weighted means of maximum contribution to lineage vs true


pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/weighted_mean_cluster_assignments_test_stats.pdf')
# DabTram
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtram_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(dabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#623594', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'DabTram') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# DabTram to DabTram
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(dabtramtodabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#561e59', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'DabTram to DabTram') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# DabTram to CoCl2
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtococl2_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(dabtramtococl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#A2248E', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'DabTram to CoCl2') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# DabTram to Cis
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtocis_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(dabtramtocis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#9D85BE', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'DabTram to Cis') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# CoCl2
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cocl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#0F8241', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'CoCl2') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# CoCl2 to DabTram
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2todabtram_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cocl2todabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#10413B', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'CoCl2 to DabTram') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# CoCl2 to CoCl2
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tococl2_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cocl2tococl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#6ABD45', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'CoCl2 to CoCl2') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# CoCl2 to Cis
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tocis_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cocl2tocis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#6DC49C', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'CoCl2 to Cis') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# Cis
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cis_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#C96D29', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'Cis') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# Cis to DabTram
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cistodabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#A23622', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'Cis to DabTram') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# Cis to CoCl2
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistococl2_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cistococl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#C96D29', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'Cis to CoCl2') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# Cis to Cis
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistocis_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cistocis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#FBD08C', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'Cis to Cis') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
dev.off()
---
title: "Lineage expression over time"
output: html_notebook
---

#Set working directory to appropriate folder for inputs and outputs on Google Drive
```{r, setup, include=FALSE}
#knitr::opts_knit$set(root.dir = '/Volumes/GoogleDrive/My Drive/Fasse_Shared/AJF_Drive_copy/Experiments/AJF009') # for aria's computer
knitr::opts_knit$set(root.dir = '/Volumes/GoogleDrive/.shortcut-targets-by-id/1zSqx3IzXMwt6clUjwyqmlOf4G1K53lvy/Fasse_Shared/AJF_Drive_copy/Experiments/AJF009') # for dylan's computer

#2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/ is additional path for outputs

```

#Initialize
```{r include = FALSE}
rm(list = ls())
library(dplyr)
library(Seurat)
library(ggplot2)
library(RColorBrewer)
library(ggpubr)
library(pheatmap)
library(viridis)

`%nin%` = Negate(`%in%`)
```

# Load data
```{r}
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/all_data_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Preprocess_GEX/second_timepoint_merged.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Filtering_cDNA/resistant_lineage_lists.RData')

load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/cis_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/cocl2_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/dabtram_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/dabtram_both_times_final_lineages.RData')

```

#Plot cell cycle
```{r}

# view cell cycle scores and phase assignments
DimPlot(all_data, group.by = "Phase")
DimPlot(all_data)
FeaturePlot(object = all_data, reduction = 'umap', features = ("nFeature_RNA"), pt.size = 1, combine = T, order = TRUE)
DimPlot(all_data, group.by = "OG_condition", cols = c('dabtram' = '#623594', 'cocl2' = '#0F8241', 'cis' = '#C96D29', 'dabtramtodabtram' = '#561E59', 'dabtramtococl2' = '#A2248E', 'dabtramtocis' = '#9D85BE', 'cocl2todabtram' = '#10413B', 'cocl2tococl2' = '#6ABD45', 'cocl2tocis' = '#6DC49C', 'cistodabtram' = '#A23622', 'cistococl2' = '#F49129', 'cistocis' = '#FBD08C'))

```

# Markers within each first drug object to find subgroups (ie analogous to NGFR/EGFR)
```{r include = FALSE}
#load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/all_data_markers.RData')
cis_markers <- FindAllMarkers(cis, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
cocl2_markers <- FindAllMarkers(cocl2, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
dabtram_markers <- FindAllMarkers(dabtram, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)

all_data.markers <- FindAllMarkers(all_data, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25) 
save(all_data.markers, cis_markers, cocl2_markers, dabtram_markers, file = 'all_data_markers.RData')
```

# need to make Idents metadata object which says if the cells are included in the combined lins list, or if they were filtered
```{r}
#find lineages that are maintained at both dabtram timepoints
fivecell_cDNA$DabTramMaintained <- Reduce(intersect, list(fivecell_cDNA$DabTram, fivecell_cDNA$DabTramtoDabTram))

filtered_meta <- rep(0, length(names(all_data$Lineage)))

#specify which cells are in lineages that pass filtering for that condition
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% combined_lins_list$DabTram)] <- 'Resistant to DabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% combined_lins_list$DabTramtoDabTram)] <- 'Resistant to DabTramtoDabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %in% combined_lins_list$DabTramtoCoCl2)] <- 'Resistant to DabTramtoCoCl2'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %in% combined_lins_list$DabTramtoCis)] <- 'Resistant to DabTramtoCis'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %in% combined_lins_list$CoCl2)] <- 'Resistant to CoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %in% combined_lins_list$CoCl2toDabTram)] <- 'Resistant to CoCl2toDabTram'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %in% combined_lins_list$CoCl2toCoCl2)] <- 'Resistant to CoCl2toCoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %in% combined_lins_list$CoCl2toCis)] <- 'Resistant to CoCl2toCis'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %in% combined_lins_list$Cis)] <- 'Resistant to Cis'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %in% combined_lins_list$CistoDabTram)] <- 'Resistant to CistoDabTram'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %in% combined_lins_list$CistoCoCl2)] <- 'Resistant to CistoCoCl2'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %in% combined_lins_list$CistoCis)] <- 'Resistant to CistoCis'

#specify which cells are in lineages of more than 5 cells
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% fivecell_cDNA$DabTram)] <- 'Large Resistant to DabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramtoDabTram)] <- 'Large Resistant to DabTramtoDabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %in% fivecell_cDNA$DabTramtoCoCl2)] <- 'Large Resistant to DabTramtoCoCl2'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %in% fivecell_cDNA$DabTramtoCis)] <- 'Large Resistant to DabTramtoCis'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %in% fivecell_cDNA$CoCl2)] <- 'Large Resistant to CoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %in% fivecell_cDNA$CoCl2toDabTram)] <- 'Large Resistant to CoCl2toDabTram'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %in% fivecell_cDNA$CoCl2toCoCl2)] <- 'Large Resistant to CoCl2toCoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %in% fivecell_cDNA$CoCl2toCis)] <- 'Large Resistant to CoCl2toCis'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %in% fivecell_cDNA$Cis)] <- 'Large Resistant to Cis'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %in% fivecell_cDNA$CistoDabTram)] <- 'Large Resistant to CistoDabTram'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %in% fivecell_cDNA$CistoCoCl2)] <- 'Large Resistant to CistoCoCl2'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %in% fivecell_cDNA$CistoCis)] <- 'Large Resistant to CistoCis'

# filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramMaintained)] <- 'Maintained Resistant to DabTram'
# filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramMaintained)] <- 'Maintained Resistant to DabTramtoDabTram'

#specify which cells are in lineages that did not pass filtering
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %nin% combined_lins_list$DabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %nin% combined_lins_list$DabTramtoDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %nin% combined_lins_list$DabTramtoCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %nin% combined_lins_list$DabTramtoCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %nin% combined_lins_list$CoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %nin% combined_lins_list$CoCl2toDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %nin% combined_lins_list$CoCl2toCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %nin% combined_lins_list$CoCl2toCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %nin% combined_lins_list$Cis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %nin% combined_lins_list$CistoDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %nin% combined_lins_list$CistoCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %nin% combined_lins_list$CistoCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 

#specify which cells had zero or multiple barcodes
filtered_meta[which(all_data$Lineage %in% c("No Barcode", "Still multiple"))] <- 'No Barcode'

print(table(filtered_meta))

```

```{r}
all_data$Resistant_filtered <- filtered_meta
Idents(all_data) <- all_data$Resistant_filtered
```

```{r}
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/test_plot.pdf', height = 10, width = 20)
DimPlot(all_data, group.by = 'ident', cols = )
DimPlot(all_data, group.by = "Lineage") + theme(legend.position = 'none')
dev.off()

```

```{r}
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/test_violin.pdf', height = 10, width = 30)

VlnPlot(all_data, features = 'NGFR', idents = 'Maintained Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'EGFR', idents = 'Maintained Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'NGFR', idents = 'Maintained Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'EGFR', idents = 'Maintained Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)

VlnPlot(all_data, features = 'NGFR', idents = 'Large Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'EGFR', idents = 'Large Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'NGFR', idents = 'Large Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'EGFR', idents = 'Large Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)

dev.off()
#VlnPlot(all_data, features = 'NGFR', idents = all_data$OG_condition == 'dabtram', group.by = "lineage")
```

#Looking into the one lineage that switches ngfr -> egfr
```{r}

switch_lin_dabtram <- names(all_data$orig.ident[all_data$Lineage == "Lin171516" & all_data$OG_condition == 'dabtram'])
switch_lin_dabtramtodabtram <- names(all_data$orig.ident[all_data$Lineage == "Lin171516" & all_data$OG_condition == 'dabtramtodabtram'])

DimPlot(all_data, group.by = "OG_condition", cols = c('dabtram' = '#623594', 'cocl2' = '#0F8241', 'cis' = '#C96D29', 'dabtramtodabtram' = '#561E59', 'dabtramtococl2' = '#A2248E', 'dabtramtocis' = '#9D85BE', 'cocl2todabtram' = '#10413B', 'cocl2tococl2' = '#6ABD45', 'cocl2tocis' = '#6DC49C', 'cistodabtram' = '#A23622', 'cistococl2' = '#F49129', 'cistocis' = '#FBD08C'))
DimPlot(all_data, cells.highlight = list(switch_lin_dabtram), cols.highlight = c('red'))
DimPlot(all_data, cells.highlight = list(switch_lin_dabtram, switch_lin_dabtramtodabtram), cols.highlight = c('blue', 'red'))

switch_lin <- names(dabtram$orig.ident[dabtram$Lineage == "Lin171516"])
DimPlot(dabtram)
DimPlot(dabtram, cells.highlight = list(switch_lin))

```

#from dabtram_both_times, force 2 clusters in umap, plot vs ngfr egfr, find markers of these 2
```{r}

dabtram_both_times_markers <- FindAllMarkers(dabtram_both_times, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
DimPlot(dabtram_both_times)
DimPlot(dabtram_both_times, group.by = 'OG_condition')
FeaturePlot(dabtram_both_times, features = c('NGFR', 'EGFR', 'nFeature_RNA'))
```

#Assign cluster assignments per lineage, find average score per lineage - make plots in order
```{r}
#average cell assignments per lineage in dabtram_maintained

#want to do a stacked barplot here-- copy paste code from condition clustering

#get lineage and cluster data from seurat object, switch cluster identifiers from 0,1 to -1,1 (egfr, ngfr)
clusters_per_lin <- data.frame (Cluster = as.numeric(as.character(dabtram_both_times$seurat_clusters)), Lineage = dabtram_both_times$Lineage, condition = dabtram_both_times$OG_condition)
clusters_per_lin$Cluster[clusters_per_lin$Cluster == 0] <- -1
clusters_per_lin_list <- list()

# Need to get percent values of EGFR for the lineage after the first treatment
for (i in fivecell_cDNA$DabTram){
  currentlin <- filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtram')
  Var1 <- c(-1,1)
  Freq <- c(sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtram')$Cluster == -1), # EGFR
  sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtram')$Cluster == 1))
  clusters_per_lin_list[[i]] <- data.frame('Var1' = Var1, 'Freq' = Freq)
  clusters_per_lin_list[[i]]$Score <- weighted.mean(as.numeric(as.character(clusters_per_lin_list[[i]]$Var1)), clusters_per_lin_list[[i]]$Freq)
}

# Build a dataframe for plotting based on % EGFR or NGFR per lineage after first treatment
clusters_per_lin_df <- data.frame(matrix(ncol = 5, nrow = 0))
colnames(clusters_per_lin_df) <- c('Lineage', 'Percent_cells', 'Num_cells', 'Score')
for (i in names(clusters_per_lin_list)){
    clusters_per_lin_df <- rbind(clusters_per_lin_df, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == 1]/sum(clusters_per_lin_list[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == 1]), 'Score' = clusters_per_lin_list[[i]]$Score[1], 'Cluster' = 'NGFR')) # Add NGFR row
  clusters_per_lin_df <- rbind(clusters_per_lin_df, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == -1]/sum(clusters_per_lin_list[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == -1]), 'Score' = clusters_per_lin_list[[i]]$Score[1], 'Cluster' = 'EGFR')) # Add EGFR row
}

# Reorder so that EGFR dominant lineages are plot first
clusters_per_lin_df <- clusters_per_lin_df[with(clusters_per_lin_df, order(-Score,Num_cells )),]
clusters_per_lin_df$Lineage <- factor(clusters_per_lin_df$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))

# make list object for after second treatment
clusters_per_lin_list_sec <- list()

# Need to get percent values of EGFR for the lineage after the second treatment
for (i in fivecell_cDNA$DabTram){
  currentlin <- filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtramtodabtram')
  Var1 <- c(-1,1)
  Freq <- c(sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtramtodabtram')$Cluster == -1), # EGFR
  sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtramtodabtram')$Cluster == 1))
  clusters_per_lin_list_sec[[i]] <- data.frame('Var1' = Var1, 'Freq' = Freq)
  clusters_per_lin_list_sec[[i]]$Score <- weighted.mean(as.numeric(as.character(clusters_per_lin_list_sec[[i]]$Var1)), clusters_per_lin_list_sec[[i]]$Freq)
}

# Build a dataframe for plotting based on % EGFR or NGFR per lineage after second treatment
clusters_per_lin_df_sec <- data.frame(matrix(ncol = 5, nrow = 0))
colnames(clusters_per_lin_df_sec) <- c('Lineage', 'Percent_cells', 'Num_cells', 'Score', 'Cluster')
for (i in names(clusters_per_lin_list)){

  if (sum(clusters_per_lin_list_sec[[i]]$Freq) < 5){
    clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 1, 'Num_cells' = 0, 'Score' = 0, 'Cluster' = 'Died')) # Add Lineage died row
    clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 0, 'Num_cells' = 0, 'Score' = 0, 'Cluster' = 'NGFR')) # Add NGFR row
    clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 0, 'Num_cells' = 0, 'Score' = 0, 'Cluster' = 'EGFR')) # Add EGFR row
  }else{ # Actually calculate percentages since the lineage survived/is more than 5 cells
      clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 0, 'Num_cells' = 0, 'Score' = clusters_per_lin_list_sec[[i]]$Score[1], 'Cluster' = 'Died')) # Add Lineage died row
      clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == 1]/sum(clusters_per_lin_list_sec[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == 1]), 'Score' = clusters_per_lin_list_sec[[i]]$Score[1], 'Cluster' = 'NGFR')) # Add NGFR row
      clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == -1]/sum(clusters_per_lin_list_sec[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == -1]), 'Score' = clusters_per_lin_list_sec[[i]]$Score[1], 'Cluster' = 'EGFR')) # Add EGFR row
    }
}

# Reorder so that EGFR dominant lineages are plot first
clusters_per_lin_df_sec$Lineage <- factor(clusters_per_lin_df_sec$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))

# Plot
p1 <- ggplot(clusters_per_lin_df, aes(y = Percent_cells, x = Lineage, fill = Cluster)) + geom_bar(stat = 'identity', position = position_fill(reverse = T), color = '#D3D3D3') + scale_fill_manual(values = c("#000000","#FFFFFF")) + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
p2 <- ggplot(clusters_per_lin_df_sec, aes(y = Percent_cells, x = Lineage, fill = Cluster)) + geom_bar(stat = 'identity', position = position_fill(reverse = T), color = '#D3D3D3') + scale_fill_manual(values = c("#FF0000","#000000", '#FFFFFF')) + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/stacked_bar_EGFR_NGFR_Died.pdf')
ggarrange(p1,p2, nrow = 2)
dev.off()
```

# Plot the number of cells per lineage in second drug for lineages that survived in DabTram
```{r}
# Get the number of cells in the DabTram resistant lineages in the conditions that got DabTram first
dabtramtococl2_dt_resist_lins <- list()
dabtramtocis_dt_resist_lins <- list()

for (i in fivecell_cDNA$DabTram){
  dabtramtococl2_dt_resist_lins[[i]] <- all_data$Lineage[all_data$Lineage == i & all_data$OG_condition == 'dabtramtococl2']
  dabtramtocis_dt_resist_lins[[i]] <- all_data$Lineage[all_data$Lineage == i & all_data$OG_condition == 'dabtramtocis']
}

# convert list to dataframe
dabtramtococl2_dt_resist_lins_df <- data.frame(num_cells = lengths(dabtramtococl2_dt_resist_lins))
dabtramtococl2_dt_resist_lins_df$Lineage = rownames(dabtramtococl2_dt_resist_lins_df)
dabtramtococl2_dt_resist_lins_df$Lineage <- factor(dabtramtococl2_dt_resist_lins_df$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))

dabtramtocis_dt_resist_lins_df <- data.frame(num_cells = lengths(dabtramtocis_dt_resist_lins))
dabtramtocis_dt_resist_lins_df$Lineage = rownames(dabtramtocis_dt_resist_lins_df)
dabtramtocis_dt_resist_lins_df$Lineage <- factor(dabtramtocis_dt_resist_lins_df$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))

# Plot 
p3 <- ggplot(dabtramtococl2_dt_resist_lins_df, aes(y = log(num_cells+1), x = Lineage)) + geom_col(fill = '#A2248E') + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + labs(title = 'DabTram to CoCl2')

p4 <- ggplot(dabtramtocis_dt_resist_lins_df, aes(y = log(num_cells+1), x = Lineage)) + geom_col(fill = '#9D85BE') + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + labs(title = 'DabTram to Cis') 

pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/stacked_bar_EGFR_NGFR_Died_w_other_second_drugs.pdf', height = 14)
ggarrange(p1+ theme(legend.position = 'bottom'),p2+ theme(legend.position = 'bottom'),p3,p4, nrow = 4)
dev.off()

# Also look at reads in gDNA of these plots?????
```

# Look at whether lineages cluster together in each individual condition - Starting with DabTram
```{r}
rm(dabtram, cis, cocl2) # Remove old seurat objects that are no longer needed
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtram
dabtram <- subset(all_data, idents = 'dabtram') # Subset down to the dabtram object

ElbowPlot(dabtram) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
dabtram <- FindNeighbors(dabtram, dims = 1:10)
dabtram <- FindClusters(dabtram, resolution = 0.5)
dabtram <- RunUMAP(dabtram, dims = 1:10)
DimPlot(dabtram, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
dabtram_lin_clust_list <- list()

for (i in fivecell_cDNA$DabTram){
  temp_df <- as.data.frame(table(Idents(dabtram)[dabtram$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  dabtram_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
dabtram_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  dabtram_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$DabTram){
    num_cells <- length(dabtram$Lineage[dabtram$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(dabtram), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    dabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the dabtram simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtram <- mean(unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtram_sim <- sapply(1:length(dabtram_lin_clust_rand_list), function(y)
  mean(unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtram <- (mean_dabtram-mean(means_dabtram_sim))/sd(means_dabtram_sim)
pval_mean_dabtram <- pnorm(z_mean_dabtram, mean(means_dabtram_sim), sd(means_dabtram_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtram <- weighted.mean(unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtram_sim <- sapply(1:length(dabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtram <- (weighted_mean_dabtram-mean(weighted_means_dabtram_sim))/sd(weighted_means_dabtram_sim)
pval_wmean_dabtram <- pnorm(z_wmean_dabtram, mean(weighted_means_dabtram_sim), sd(weighted_means_dabtram_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtram <- c()
for (i in 1:length(means_dabtram_sim)){
  sim_maxes <- unlist(lapply(dabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_dabtram <- cbind(ttest_pval_dabtram,t.test(x = unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(dabtram_lin_clust_list, dabtram_lin_clust_rand_list,z_mean_dabtram,pval_mean_dabtram, z_wmean_dabtram, ttest_pval_dabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtram_sim_results.RData')
rm(dabtram)
```

# Look at whether lineages cluster together in each individual condition -  DabTramtoDabTram
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtramtodabtram
dabtramtodabtram <- subset(all_data, idents = 'dabtramtodabtram') # Subset down to the dabtramtodabtram object

ElbowPlot(dabtramtodabtram) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
dabtramtodabtram <- FindNeighbors(dabtramtodabtram, dims = 1:10)
dabtramtodabtram <- FindClusters(dabtramtodabtram, resolution = 0.5)
dabtramtodabtram <- RunUMAP(dabtramtodabtram, dims = 1:10)
DimPlot(dabtramtodabtram, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
dabtramtodabtram_lin_clust_list <- list()

for (i in fivecell_cDNA$DabTramtoDabTram){
  temp_df <- as.data.frame(table(Idents(dabtramtodabtram)[dabtramtodabtram$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  dabtramtodabtram_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
dabtramtodabtram_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  dabtramtodabtram_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$DabTramtoDabTram){
    num_cells <- length(dabtramtodabtram$Lineage[dabtramtodabtram$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(dabtramtodabtram), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    dabtramtodabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```

# Significance testing of the dabtramtodabtram simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtramtodabtram <- mean(unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtramtodabtram_sim <- sapply(1:length(dabtramtodabtram_lin_clust_rand_list), function(y)
  mean(unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtramtodabtram <- (mean_dabtramtodabtram-mean(means_dabtramtodabtram_sim))/sd(means_dabtramtodabtram_sim)
pval_mean_dabtramtodabtram <- pnorm(z_mean_dabtramtodabtram, mean(means_dabtramtodabtram_sim), sd(means_dabtramtodabtram_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtramtodabtram <- weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtramtodabtram_sim <- sapply(1:length(dabtramtodabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtramtodabtram <- (weighted_mean_dabtramtodabtram-mean(weighted_means_dabtramtodabtram_sim))/sd(weighted_means_dabtramtodabtram_sim)
pval_wmean_dabtramtodabtram <- pnorm(z_wmean_dabtramtodabtram, mean(weighted_means_dabtramtodabtram_sim), sd(weighted_means_dabtramtodabtram_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtramtodabtram <- c()
for (i in 1:length(means_dabtramtodabtram_sim)){
  sim_maxes <- unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_dabtramtodabtram <- cbind(ttest_pval_dabtramtodabtram,t.test(x = unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(dabtramtodabtram_lin_clust_list, dabtramtodabtram_lin_clust_rand_list,z_mean_dabtramtodabtram,pval_mean_dabtramtodabtram, z_wmean_dabtramtodabtram, ttest_pval_dabtramtodabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtodabtram_sim_results.RData')
rm(dabtramtodabtram)
```

# Look at whether lineages cluster together in each individual condition -  DabTramtoCis
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtramtocis
dabtramtocis <- subset(all_data, idents = 'dabtramtocis') # Subset down to the dabtramtocis object

ElbowPlot(dabtramtocis) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
dabtramtocis <- FindNeighbors(dabtramtocis, dims = 1:10)
dabtramtocis <- FindClusters(dabtramtocis, resolution = 0.5)
dabtramtocis <- RunUMAP(dabtramtocis, dims = 1:10)
DimPlot(dabtramtocis, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
dabtramtocis_lin_clust_list <- list()

for (i in fivecell_cDNA$DabTramtoCis){
  temp_df <- as.data.frame(table(Idents(dabtramtocis)[dabtramtocis$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  dabtramtocis_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
dabtramtocis_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  dabtramtocis_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$DabTramtoCis){
    num_cells <- length(dabtramtocis$Lineage[dabtramtocis$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(dabtramtocis), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    dabtramtocis_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```

# Significance testing of the dabtramtocis simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtramtocis <- mean(unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtramtocis_sim <- sapply(1:length(dabtramtocis_lin_clust_rand_list), function(y)
  mean(unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtramtocis <- (mean_dabtramtocis-mean(means_dabtramtocis_sim))/sd(means_dabtramtocis_sim)
pval_mean_dabtramtocis <- pnorm(z_mean_dabtramtocis, mean(means_dabtramtocis_sim), sd(means_dabtramtocis_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtramtocis <- weighted.mean(unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtocis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtramtocis_sim <- sapply(1:length(dabtramtocis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtramtocis <- (weighted_mean_dabtramtocis-mean(weighted_means_dabtramtocis_sim))/sd(weighted_means_dabtramtocis_sim)
pval_wmean_dabtramtocis <- pnorm(z_wmean_dabtramtocis, mean(weighted_means_dabtramtocis_sim), sd(weighted_means_dabtramtocis_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtramtocis <- c()
for (i in 1:length(means_dabtramtocis_sim)){
  sim_maxes <- unlist(lapply(dabtramtocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_dabtramtocis <- cbind(ttest_pval_dabtramtocis,t.test(x = unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtramtocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(dabtramtocis_lin_clust_list, dabtramtocis_lin_clust_rand_list,z_mean_dabtramtocis,pval_mean_dabtramtocis, z_wmean_dabtramtocis, ttest_pval_dabtramtocis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtocis_sim_results.RData')
rm(dabtramtocis)
```

# Look at whether lineages cluster together in each individual condition -  DabTramtoCis
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtramtococl2
dabtramtococl2 <- subset(all_data, idents = 'dabtramtococl2') # Subset down to the dabtramtococl2 object

ElbowPlot(dabtramtococl2) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
dabtramtococl2 <- FindNeighbors(dabtramtococl2, dims = 1:10)
dabtramtococl2 <- FindClusters(dabtramtococl2, resolution = 0.5)
dabtramtococl2 <- RunUMAP(dabtramtococl2, dims = 1:10)
DimPlot(dabtramtococl2, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
dabtramtococl2_lin_clust_list <- list()

for (i in fivecell_cDNA$DabTramtoCoCl2){
  temp_df <- as.data.frame(table(Idents(dabtramtococl2)[dabtramtococl2$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  dabtramtococl2_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
dabtramtococl2_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  dabtramtococl2_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$DabTramtoCoCl2){
    num_cells <- length(dabtramtococl2$Lineage[dabtramtococl2$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(dabtramtococl2), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    dabtramtococl2_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```

# Significance testing of the dabtramtococl2 simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtramtococl2 <- mean(unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtramtococl2_sim <- sapply(1:length(dabtramtococl2_lin_clust_rand_list), function(y)
  mean(unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtramtococl2 <- (mean_dabtramtococl2-mean(means_dabtramtococl2_sim))/sd(means_dabtramtococl2_sim)
pval_mean_dabtramtococl2 <- pnorm(z_mean_dabtramtococl2, mean(means_dabtramtococl2_sim), sd(means_dabtramtococl2_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtramtococl2 <- weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtococl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtramtococl2_sim <- sapply(1:length(dabtramtococl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtramtococl2 <- (weighted_mean_dabtramtococl2-mean(weighted_means_dabtramtococl2_sim))/sd(weighted_means_dabtramtococl2_sim)
pval_wmean_dabtramtococl2 <- pnorm(z_wmean_dabtramtococl2, mean(weighted_means_dabtramtococl2_sim), sd(weighted_means_dabtramtococl2_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtramtococl2 <- c()
for (i in 1:length(means_dabtramtococl2_sim)){
  sim_maxes <- unlist(lapply(dabtramtococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_dabtramtococl2 <- cbind(ttest_pval_dabtramtococl2,t.test(x = unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtramtococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(dabtramtococl2_lin_clust_list, dabtramtococl2_lin_clust_rand_list,z_mean_dabtramtococl2,pval_mean_dabtramtococl2, z_wmean_dabtramtococl2, ttest_pval_dabtramtococl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtococl2_sim_results.RData')
rm(dabtramtococl2)
```

# Look at whether lineages cluster together in each individual condition - Starting with DabTram
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2
cocl2 <- subset(all_data, idents = 'cocl2') # Subset down to the cocl2 object

ElbowPlot(cocl2) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cocl2 <- FindNeighbors(cocl2, dims = 1:10)
cocl2 <- FindClusters(cocl2, resolution = 0.5)
cocl2 <- RunUMAP(cocl2, dims = 1:10)
DimPlot(cocl2, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cocl2_lin_clust_list <- list()

for (i in fivecell_cDNA$CoCl2){
  temp_df <- as.data.frame(table(Idents(cocl2)[cocl2$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cocl2_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cocl2_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cocl2_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CoCl2){
    num_cells <- length(cocl2$Lineage[cocl2$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cocl2), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cocl2_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cocl2 simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2 <- mean(unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2_sim <- sapply(1:length(cocl2_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2 <- (mean_cocl2-mean(means_cocl2_sim))/sd(means_cocl2_sim)
pval_mean_cocl2 <- pnorm(z_mean_cocl2, mean(means_cocl2_sim), sd(means_cocl2_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2 <- weighted.mean(unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2_sim <- sapply(1:length(cocl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2 <- (weighted_mean_cocl2-mean(weighted_means_cocl2_sim))/sd(weighted_means_cocl2_sim)
pval_wmean_cocl2 <- pnorm(z_wmean_cocl2, mean(weighted_means_cocl2_sim), sd(weighted_means_cocl2_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2 <- c()
for (i in 1:length(means_cocl2_sim)){
  sim_maxes <- unlist(lapply(cocl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cocl2 <- cbind(ttest_pval_cocl2,t.test(x = unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cocl2_lin_clust_list, cocl2_lin_clust_rand_list,z_mean_cocl2,pval_mean_cocl2, z_wmean_cocl2, ttest_pval_cocl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2_sim_results.RData')
rm(cocl2)
```

# Look at whether lineages cluster together in each individual condition - Starting with CoCl2toCoCl2
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2tococl2
cocl2tococl2 <- subset(all_data, idents = 'cocl2tococl2') # Subset down to the cocl2tococl2 object

ElbowPlot(cocl2tococl2) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cocl2tococl2 <- FindNeighbors(cocl2tococl2, dims = 1:10)
cocl2tococl2 <- FindClusters(cocl2tococl2, resolution = 0.5)
cocl2tococl2 <- RunUMAP(cocl2tococl2, dims = 1:10)
DimPlot(cocl2tococl2, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cocl2tococl2_lin_clust_list <- list()

for (i in fivecell_cDNA$CoCl2toCoCl2){
  temp_df <- as.data.frame(table(Idents(cocl2tococl2)[cocl2tococl2$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cocl2tococl2_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cocl2tococl2_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cocl2tococl2_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CoCl2toCoCl2){
    num_cells <- length(cocl2tococl2$Lineage[cocl2tococl2$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cocl2tococl2), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cocl2tococl2_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cocl2tococl2 simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2tococl2 <- mean(unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2tococl2_sim <- sapply(1:length(cocl2tococl2_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2tococl2 <- (mean_cocl2tococl2-mean(means_cocl2tococl2_sim))/sd(means_cocl2tococl2_sim)
pval_mean_cocl2tococl2 <- pnorm(z_mean_cocl2tococl2, mean(means_cocl2tococl2_sim), sd(means_cocl2tococl2_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2tococl2 <- weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tococl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2tococl2_sim <- sapply(1:length(cocl2tococl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2tococl2 <- (weighted_mean_cocl2tococl2-mean(weighted_means_cocl2tococl2_sim))/sd(weighted_means_cocl2tococl2_sim)
pval_wmean_cocl2tococl2 <- pnorm(z_wmean_cocl2tococl2, mean(weighted_means_cocl2tococl2_sim), sd(weighted_means_cocl2tococl2_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2tococl2 <- c()
for (i in 1:length(means_cocl2tococl2_sim)){
  sim_maxes <- unlist(lapply(cocl2tococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cocl2tococl2 <- cbind(ttest_pval_cocl2tococl2,t.test(x = unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2tococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cocl2tococl2_lin_clust_list, cocl2tococl2_lin_clust_rand_list,z_mean_cocl2tococl2,pval_mean_cocl2tococl2, z_wmean_cocl2tococl2, ttest_pval_cocl2tococl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tococl2_sim_results.RData')
rm(cocl2tococl2)
```

# Look at whether lineages cluster together in each individual condition - Starting with CoCl2toCis
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2tocis
cocl2tocis <- subset(all_data, idents = 'cocl2tocis') # Subset down to the cocl2tocis object

ElbowPlot(cocl2tocis) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cocl2tocis <- FindNeighbors(cocl2tocis, dims = 1:10)
cocl2tocis <- FindClusters(cocl2tocis, resolution = 0.5)
cocl2tocis <- RunUMAP(cocl2tocis, dims = 1:10)
DimPlot(cocl2tocis, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cocl2tocis_lin_clust_list <- list()

for (i in fivecell_cDNA$CoCl2toCis){
  temp_df <- as.data.frame(table(Idents(cocl2tocis)[cocl2tocis$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cocl2tocis_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cocl2tocis_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cocl2tocis_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CoCl2toCis){
    num_cells <- length(cocl2tocis$Lineage[cocl2tocis$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cocl2tocis), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cocl2tocis_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cocl2tocis simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2tocis <- mean(unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2tocis_sim <- sapply(1:length(cocl2tocis_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2tocis <- (mean_cocl2tocis-mean(means_cocl2tocis_sim))/sd(means_cocl2tocis_sim)
pval_mean_cocl2tocis <- pnorm(z_mean_cocl2tocis, mean(means_cocl2tocis_sim), sd(means_cocl2tocis_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2tocis <- weighted.mean(unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tocis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2tocis_sim <- sapply(1:length(cocl2tocis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2tocis <- (weighted_mean_cocl2tocis-mean(weighted_means_cocl2tocis_sim))/sd(weighted_means_cocl2tocis_sim)
pval_wmean_cocl2tocis <- pnorm(z_wmean_cocl2tocis, mean(weighted_means_cocl2tocis_sim), sd(weighted_means_cocl2tocis_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2tocis <- c()
for (i in 1:length(means_cocl2tocis_sim)){
  sim_maxes <- unlist(lapply(cocl2tocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cocl2tocis <- cbind(ttest_pval_cocl2tocis,t.test(x = unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2tocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cocl2tocis_lin_clust_list, cocl2tocis_lin_clust_rand_list,z_mean_cocl2tocis,pval_mean_cocl2tocis, z_wmean_cocl2tocis, ttest_pval_cocl2tocis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tocis_sim_results.RData')
rm(cocl2tocis)
```

# Look at whether lineages cluster together in each individual condition - Starting with CoCl2toDabTram
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2todabtram
cocl2todabtram <- subset(all_data, idents = 'cocl2todabtram') # Subset down to the cocl2todabtram object

ElbowPlot(cocl2todabtram) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cocl2todabtram <- FindNeighbors(cocl2todabtram, dims = 1:10)
cocl2todabtram <- FindClusters(cocl2todabtram, resolution = 0.5)
cocl2todabtram <- RunUMAP(cocl2todabtram, dims = 1:10)
DimPlot(cocl2todabtram, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cocl2todabtram_lin_clust_list <- list()

for (i in fivecell_cDNA$CoCl2toDabTram){
  temp_df <- as.data.frame(table(Idents(cocl2todabtram)[cocl2todabtram$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cocl2todabtram_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cocl2todabtram_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cocl2todabtram_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CoCl2toDabTram){
    num_cells <- length(cocl2todabtram$Lineage[cocl2todabtram$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cocl2todabtram), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cocl2todabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cocl2todabtram simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2todabtram <- mean(unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2todabtram_sim <- sapply(1:length(cocl2todabtram_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2todabtram <- (mean_cocl2todabtram-mean(means_cocl2todabtram_sim))/sd(means_cocl2todabtram_sim)
pval_mean_cocl2todabtram <- pnorm(z_mean_cocl2todabtram, mean(means_cocl2todabtram_sim), sd(means_cocl2todabtram_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2todabtram <- weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2todabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2todabtram_sim <- sapply(1:length(cocl2todabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2todabtram <- (weighted_mean_cocl2todabtram-mean(weighted_means_cocl2todabtram_sim))/sd(weighted_means_cocl2todabtram_sim)
pval_wmean_cocl2todabtram <- pnorm(z_wmean_cocl2todabtram, mean(weighted_means_cocl2todabtram_sim), sd(weighted_means_cocl2todabtram_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2todabtram <- c()
for (i in 1:length(means_cocl2todabtram_sim)){
  sim_maxes <- unlist(lapply(cocl2todabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cocl2todabtram <- cbind(ttest_pval_cocl2todabtram,t.test(x = unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2todabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cocl2todabtram_lin_clust_list, cocl2todabtram_lin_clust_rand_list,z_mean_cocl2todabtram,pval_mean_cocl2todabtram, z_wmean_cocl2todabtram, ttest_pval_cocl2todabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2todabtram_sim_results.RData')
rm(cocl2todabtram)
```

# Look at whether lineages cluster together in each individual condition - Starting with Cis
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cis
cis <- subset(all_data, idents = 'cis') # Subset down to the cis object

ElbowPlot(cis) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cis <- FindNeighbors(cis, dims = 1:10)
cis <- FindClusters(cis, resolution = 0.5)
cis <- RunUMAP(cis, dims = 1:10)
DimPlot(cis, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cis_lin_clust_list <- list()

for (i in fivecell_cDNA$Cis){
  temp_df <- as.data.frame(table(Idents(cis)[cis$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cis_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cis_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cis_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$Cis){
    num_cells <- length(cis$Lineage[cis$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cis), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cis_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cis simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cis <- mean(unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cis_sim <- sapply(1:length(cis_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cis <- (mean_cis-mean(means_cis_sim))/sd(means_cis_sim)
pval_mean_cis <- pnorm(z_mean_cis, mean(means_cis_sim), sd(means_cis_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cis <- weighted.mean(unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cis_sim <- sapply(1:length(cis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cis <- (weighted_mean_cis-mean(weighted_means_cis_sim))/sd(weighted_means_cis_sim)
pval_wmean_cis <- pnorm(z_wmean_cis, mean(weighted_means_cis_sim), sd(weighted_means_cis_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cis <- c()
for (i in 1:length(means_cis_sim)){
  sim_maxes <- unlist(lapply(cis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cis <- cbind(ttest_pval_cis,t.test(x = unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cis_lin_clust_list, cis_lin_clust_rand_list,z_mean_cis,pval_mean_cis, z_wmean_cis, ttest_pval_cis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cis_sim_results.RData')
rm(cis)
```


# Look at whether lineages cluster together in each individual condition - Starting with cistocis
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cistocis
cistocis <- subset(all_data, idents = 'cistocis') # Subset down to the cistocis object

ElbowPlot(cistocis) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cistocis <- FindNeighbors(cistocis, dims = 1:10)
cistocis <- FindClusters(cistocis, resolution = 0.5)
cistocis <- RunUMAP(cistocis, dims = 1:10)
DimPlot(cistocis, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cistocis_lin_clust_list <- list()

for (i in fivecell_cDNA$CistoCis){
  temp_df <- as.data.frame(table(Idents(cistocis)[cistocis$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cistocis_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cistocis_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cistocis_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CistoCis){
    num_cells <- length(cistocis$Lineage[cistocis$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cistocis), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cistocis_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cistocis simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cistocis <- mean(unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cistocis_sim <- sapply(1:length(cistocis_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cistocis <- (mean_cistocis-mean(means_cistocis_sim))/sd(means_cistocis_sim)
pval_mean_cistocis <- pnorm(z_mean_cistocis, mean(means_cistocis_sim), sd(means_cistocis_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cistocis <- weighted.mean(unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistocis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cistocis_sim <- sapply(1:length(cistocis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cistocis <- (weighted_mean_cistocis-mean(weighted_means_cistocis_sim))/sd(weighted_means_cistocis_sim)
pval_wmean_cistocis <- pnorm(z_wmean_cistocis, mean(weighted_means_cistocis_sim), sd(weighted_means_cistocis_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cistocis <- c()
for (i in 1:length(means_cistocis_sim)){
  sim_maxes <- unlist(lapply(cistocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cistocis <- cbind(ttest_pval_cistocis,t.test(x = unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cistocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cistocis_lin_clust_list, cistocis_lin_clust_rand_list,z_mean_cistocis,pval_mean_cistocis, z_wmean_cistocis, ttest_pval_cistocis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistocis_sim_results.RData')
rm(cistocis)
```


# Look at whether lineages cluster together in each individual condition - Starting with cistodabtram
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cistodabtram
cistodabtram <- subset(all_data, idents = 'cistodabtram') # Subset down to the cistodabtram object

ElbowPlot(cistodabtram) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cistodabtram <- FindNeighbors(cistodabtram, dims = 1:10)
cistodabtram <- FindClusters(cistodabtram, resolution = 0.5)
cistodabtram <- RunUMAP(cistodabtram, dims = 1:10)
DimPlot(cistodabtram, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cistodabtram_lin_clust_list <- list()

for (i in fivecell_cDNA$CistoDabTram){
  temp_df <- as.data.frame(table(Idents(cistodabtram)[cistodabtram$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cistodabtram_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cistodabtram_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cistodabtram_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CistoDabTram){
    num_cells <- length(cistodabtram$Lineage[cistodabtram$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cistodabtram), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cistodabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cistodabtram simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cistodabtram <- mean(unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cistodabtram_sim <- sapply(1:length(cistodabtram_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cistodabtram <- (mean_cistodabtram-mean(means_cistodabtram_sim))/sd(means_cistodabtram_sim)
pval_mean_cistodabtram <- pnorm(z_mean_cistodabtram, mean(means_cistodabtram_sim), sd(means_cistodabtram_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cistodabtram <- weighted.mean(unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cistodabtram_sim <- sapply(1:length(cistodabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cistodabtram <- (weighted_mean_cistodabtram-mean(weighted_means_cistodabtram_sim))/sd(weighted_means_cistodabtram_sim)
pval_wmean_cistodabtram <- pnorm(z_wmean_cistodabtram, mean(weighted_means_cistodabtram_sim), sd(weighted_means_cistodabtram_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cistodabtram <- c()
for (i in 1:length(means_cistodabtram_sim)){
  sim_maxes <- unlist(lapply(cistodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cistodabtram <- cbind(ttest_pval_cistodabtram,t.test(x = unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cistodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cistodabtram_lin_clust_list, cistodabtram_lin_clust_rand_list,z_mean_cistodabtram,pval_mean_cistodabtram, z_wmean_cistodabtram, ttest_pval_cistodabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistodabtram_sim_results.RData')
rm(cistodabtram)
```

# Look at whether lineages cluster together in each individual condition - Starting with cistococl2
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cistococl2
cistococl2 <- subset(all_data, idents = 'cistococl2') # Subset down to the cistococl2 object

ElbowPlot(cistococl2) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cistococl2 <- FindNeighbors(cistococl2, dims = 1:10)
cistococl2 <- FindClusters(cistococl2, resolution = 0.5)
cistococl2 <- RunUMAP(cistococl2, dims = 1:10)
DimPlot(cistococl2, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cistococl2_lin_clust_list <- list()

for (i in fivecell_cDNA$CistoCoCl2){
  temp_df <- as.data.frame(table(Idents(cistococl2)[cistococl2$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cistococl2_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cistococl2_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cistococl2_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CistoCoCl2){
    num_cells <- length(cistococl2$Lineage[cistococl2$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cistococl2), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cistococl2_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cistococl2 simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cistococl2 <- mean(unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cistococl2_sim <- sapply(1:length(cistococl2_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cistococl2 <- (mean_cistococl2-mean(means_cistococl2_sim))/sd(means_cistococl2_sim)
pval_mean_cistococl2 <- pnorm(z_mean_cistococl2, mean(means_cistococl2_sim), sd(means_cistococl2_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cistococl2 <- weighted.mean(unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistococl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cistococl2_sim <- sapply(1:length(cistococl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cistococl2 <- (weighted_mean_cistococl2-mean(weighted_means_cistococl2_sim))/sd(weighted_means_cistococl2_sim)
pval_wmean_cistococl2 <- pnorm(z_wmean_cistococl2, mean(weighted_means_cistococl2_sim), sd(weighted_means_cistococl2_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cistococl2 <- c()
for (i in 1:length(means_cistococl2_sim)){
  sim_maxes <- unlist(lapply(cistococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cistococl2 <- cbind(ttest_pval_cistococl2,t.test(x = unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cistococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cistococl2_lin_clust_list, cistococl2_lin_clust_rand_list,z_mean_cistococl2,pval_mean_cistococl2, z_wmean_cistococl2, ttest_pval_cistococl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistococl2_sim_results.RData')
rm(cistococl2)
```

# Build dataframe of overall simulation results
```{r}
sim_results <- rbind(
  dabtram <- data.frame("Mean_Zscore" = z_mean_dabtram, "Mean_pval" = pval_mean_dabtram, "Weighted_Mean_Zscore" = z_wmean_dabtram, "Weighted_Mean_pval" = pval_wmean_dabtram, ttest_sims_sig = sum(ttest_pval_dabtram <= 0.05)),
  dabtramtodabtram <- data.frame("Mean_Zscore" = z_mean_dabtramtodabtram, "Mean_pval" = pval_mean_dabtramtodabtram, "Weighted_Mean_Zscore" = z_wmean_dabtramtodabtram, "Weighted_Mean_pval" = pval_wmean_dabtramtodabtram, ttest_sims_sig = sum(ttest_pval_dabtramtodabtram <= 0.05)),
  dabtramtococl2 <- data.frame("Mean_Zscore" = z_mean_dabtramtococl2, "Mean_pval" = pval_mean_dabtramtococl2, "Weighted_Mean_Zscore" = z_wmean_dabtramtococl2, "Weighted_Mean_pval" = pval_wmean_dabtramtococl2, ttest_sims_sig = sum(ttest_pval_dabtramtococl2 <= 0.05)),
  dabtramtocis <- data.frame("Mean_Zscore" = z_mean_dabtramtocis, "Mean_pval" = pval_mean_dabtramtocis, "Weighted_Mean_Zscore" = z_wmean_dabtramtocis, "Weighted_Mean_pval" = pval_wmean_dabtramtocis, ttest_sims_sig = sum(ttest_pval_dabtramtocis <= 0.05)),
  cis <- data.frame("Mean_Zscore" = z_mean_cis, "Mean_pval" = pval_mean_cis, "Weighted_Mean_Zscore" = z_wmean_cis, "Weighted_Mean_pval" = pval_wmean_cis, ttest_sims_sig = sum(ttest_pval_cis <= 0.05)),
  cistodabtram <- data.frame("Mean_Zscore" = z_mean_cistodabtram, "Mean_pval" = pval_mean_cistodabtram, "Weighted_Mean_Zscore" = z_wmean_cistodabtram, "Weighted_Mean_pval" = pval_wmean_cistodabtram, ttest_sims_sig = sum(ttest_pval_cistodabtram <= 0.05)),
  cistococl2 <- data.frame("Mean_Zscore" = z_mean_cistococl2, "Mean_pval" = pval_mean_cistococl2, "Weighted_Mean_Zscore" = z_wmean_cistococl2, "Weighted_Mean_pval" = pval_wmean_cistococl2, ttest_sims_sig = sum(ttest_pval_cistococl2 <= 0.05)),
  cistocis <- data.frame("Mean_Zscore" = z_mean_cistocis, "Mean_pval" = pval_mean_cistocis, "Weighted_Mean_Zscore" = z_wmean_cistocis, "Weighted_Mean_pval" = pval_wmean_cistocis, ttest_sims_sig = sum(ttest_pval_cistocis <= 0.05)),
  cocl2 <- data.frame("Mean_Zscore" = z_mean_cocl2, "Mean_pval" = pval_mean_cocl2, "Weighted_Mean_Zscore" = z_wmean_cocl2, "Weighted_Mean_pval" = pval_wmean_cocl2, ttest_sims_sig = sum(ttest_pval_cocl2 <= 0.05)),
  cocl2todabtram <- data.frame("Mean_Zscore" = z_mean_cocl2todabtram, "Mean_pval" = pval_mean_cocl2todabtram, "Weighted_Mean_Zscore" = z_wmean_cocl2todabtram, "Weighted_Mean_pval" = pval_wmean_cocl2todabtram, ttest_sims_sig = sum(ttest_pval_cocl2todabtram <= 0.05)),
  cocl2tococl2 <- data.frame("Mean_Zscore" = z_mean_cocl2tococl2, "Mean_pval" = pval_mean_cocl2tococl2, "Weighted_Mean_Zscore" = z_wmean_cocl2tococl2, "Weighted_Mean_pval" = pval_wmean_cocl2tococl2, ttest_sims_sig = sum(ttest_pval_cocl2tococl2 <= 0.05)),
  cocl2tocis <- data.frame("Mean_Zscore" = z_mean_cocl2tocis, "Mean_pval" = pval_mean_cocl2tocis, "Weighted_Mean_Zscore" = z_wmean_cocl2tocis, "Weighted_Mean_pval" = pval_wmean_cocl2tocis, ttest_sims_sig = sum(ttest_pval_cocl2tocis <= 0.05))
)

rownames(sim_results) <- c("dabtram", "dabtramtodabtram", "dabtramtococl2", "dabtramtocis", "cis", "cistodabtram", "cistococl2", "cistocis", "cocl2", "cocl2todabtram", "cocl2tococl2", "cocl2tocis")

write.csv(sim_results, '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/sim_results.csv', row.names = T) 
```

# Load simulation data back in
```{r}
sim_results <- read.csv('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/sim_results.csv')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtram_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtodabtram_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtococl2_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtocis_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2todabtram_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tococl2_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tocis_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cis_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistodabtram_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistococl2_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistocis_sim_results.RData')

```

# Plot heatmaps of true percentage of cells per lineage in each cluster vs random simulations
```{r}

breaks <- seq(0, 1, 0.1)

# DabTram
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/proportion_clusters_heatmaps.pdf', width = 16)
dabtram_pcnts <- as.data.frame(sapply(dabtram_lin_clust_list, '[[', 3))
cell_nums <- sapply(dabtram_lin_clust_list, function(x) sum(x$Num_cells))
rownames(dabtram_pcnts) <- c("0","1","2","3","4","5","6")
colnames(dabtram_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(dabtram_pcnts,cluster_rows = F, color = inferno(11), main = "DabTram", breaks = breaks) # 
p

dabtram_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(dabtram_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(dabtram_lin_clust_rand_list))
rownames(dabtram_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(dabtram_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
dabtram_pcnts_rand <- dabtram_pcnts_rand[,p$tree_col$order]
pheatmap(dabtram_pcnts_rand,cluster_rows = F, color = inferno(11), main = "DabTram Rand", breaks = breaks, cluster_cols = F)

# DabTram to DabTram
dabtramtodabtram_pcnts <- as.data.frame(sapply(dabtramtodabtram_lin_clust_list, '[[', 3))
cell_nums <- sapply(dabtramtodabtram_lin_clust_list, function(x) sum(x$Num_cells))
rownames(dabtramtodabtram_pcnts) <- c("0","1","2","3","4","5","6")
colnames(dabtramtodabtram_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(dabtramtodabtram_pcnts,cluster_rows = F, color = inferno(11), main = "DabTram to DabTram", breaks = breaks)
p

dabtramtodabtram_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(dabtramtodabtram_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(dabtramtodabtram_lin_clust_rand_list))
rownames(dabtramtodabtram_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(dabtramtodabtram_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
dabtramtodabtram_pcnts_rand <- dabtramtodabtram_pcnts_rand[,p$tree_col$order]
pheatmap(dabtramtodabtram_pcnts_rand,cluster_rows = F, color = inferno(11), main = "DabTram to DabTram Rand", breaks = breaks, cluster_cols = F)

# DabTram to CoCl2
dabtramtococl2_pcnts <- as.data.frame(sapply(dabtramtococl2_lin_clust_list, '[[', 3))
cell_nums <- sapply(dabtramtococl2_lin_clust_list, function(x) sum(x$Num_cells))
rownames(dabtramtococl2_pcnts) <- c("0","1","2","3","4","5","6","7")
colnames(dabtramtococl2_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(dabtramtococl2_pcnts,cluster_rows = F, color = inferno(11), main = "DabTram to CoCl2", breaks = breaks)
p

dabtramtococl2_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(dabtramtococl2_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(dabtramtococl2_lin_clust_rand_list))
rownames(dabtramtococl2_pcnts_rand) <- c("0","1","2","3","4","5","6","7")
colnames(dabtramtococl2_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
dabtramtococl2_pcnts_rand <- dabtramtococl2_pcnts_rand[,p$tree_col$order]
pheatmap(dabtramtococl2_pcnts_rand,cluster_rows = F, color = inferno(11), main = "DabTram to CoCl2 Rand", breaks = breaks, cluster_cols = F)

# DabTram to Cis
dabtramtocis_pcnts <- as.data.frame(sapply(dabtramtocis_lin_clust_list, '[[', 3))
cell_nums <- sapply(dabtramtocis_lin_clust_list, function(x) sum(x$Num_cells))
rownames(dabtramtocis_pcnts) <- c("0","1","2","3","4","5","6")
colnames(dabtramtocis_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(dabtramtocis_pcnts,cluster_rows = F, color = inferno(11), main = "DabTram to Cis", breaks = breaks)
p

dabtramtocis_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(dabtramtocis_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(dabtramtocis_lin_clust_rand_list))
rownames(dabtramtocis_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(dabtramtocis_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
dabtramtocis_pcnts_rand <- dabtramtocis_pcnts_rand[,p$tree_col$order]
pheatmap(dabtramtocis_pcnts_rand,cluster_rows = F, color = inferno(11), main = "DabTram to Cis Rand", breaks = breaks, cluster_cols = F)

# CoCl2 objects
cocl2_pcnts <- as.data.frame(sapply(cocl2_lin_clust_list, '[[', 3))
cell_nums <- sapply(cocl2_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cocl2_pcnts) <- c("0","1","2","3","4","5","6","7","8")
colnames(cocl2_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cocl2_pcnts,cluster_rows = F, color = inferno(11), main = "CoCl2", breaks = breaks)
p

cocl2_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cocl2_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cocl2_lin_clust_rand_list))
rownames(cocl2_pcnts_rand) <- c("0","1","2","3","4","5","6","7","8")
colnames(cocl2_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cocl2_pcnts_rand <- cocl2_pcnts_rand[,p$tree_col$order]
pheatmap(cocl2_pcnts_rand,cluster_rows = F, color = inferno(11), main = "CoCl2 Rand", breaks = breaks, cluster_cols = F)

# CoCl2 to DabTram
cocl2todabtram_pcnts <- as.data.frame(sapply(cocl2todabtram_lin_clust_list, '[[', 3))
cell_nums <- sapply(cocl2todabtram_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cocl2todabtram_pcnts) <- c("0","1","2","3","4","5","6")
colnames(cocl2todabtram_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cocl2todabtram_pcnts,cluster_rows = F, color = inferno(11), main = "CoCl2 to DabTram", breaks = breaks)
p

cocl2todabtram_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cocl2todabtram_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cocl2todabtram_lin_clust_rand_list))
rownames(cocl2todabtram_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(cocl2todabtram_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cocl2todabtram_pcnts_rand <- cocl2todabtram_pcnts_rand[,p$tree_col$order]
pheatmap(cocl2todabtram_pcnts_rand,cluster_rows = F, color = inferno(11), main = "CoCl2 to DabTram Rand", breaks = breaks, cluster_cols = F)

# CoCl2 to CoCl2
cocl2tococl2_pcnts <- as.data.frame(sapply(cocl2tococl2_lin_clust_list, '[[', 3))
cell_nums <- sapply(cocl2tococl2_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cocl2tococl2_pcnts) <- c("0","1","2","3","4","5")
colnames(cocl2tococl2_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cocl2tococl2_pcnts,cluster_rows = F, color = inferno(11), main = "CoCl2 to CoCl2", breaks = breaks)
p

cocl2tococl2_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cocl2tococl2_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cocl2tococl2_lin_clust_rand_list))
rownames(cocl2tococl2_pcnts_rand) <- c("0","1","2","3","4","5")
colnames(cocl2tococl2_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cocl2tococl2_pcnts_rand <- cocl2tococl2_pcnts_rand[,p$tree_col$order]
pheatmap(cocl2tococl2_pcnts_rand,cluster_rows = F, color = inferno(11), main = "CoCl2 to CoCl2 Rand", breaks = breaks, cluster_cols = F)

# CoCl2 to Cis
cocl2tocis_pcnts <- as.data.frame(sapply(cocl2tocis_lin_clust_list, '[[', 3))
cell_nums <- sapply(cocl2tocis_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cocl2tocis_pcnts) <- c("0","1","2","3","4","5")
colnames(cocl2tocis_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cocl2tocis_pcnts,cluster_rows = F, color = inferno(11), main = "CoCl2 to Cis", breaks = breaks)
p

cocl2tocis_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cocl2tocis_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cocl2tocis_lin_clust_rand_list))
rownames(cocl2tocis_pcnts_rand) <- c("0","1","2","3","4","5")
colnames(cocl2tocis_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cocl2tocis_pcnts_rand <- cocl2tocis_pcnts_rand[,p$tree_col$order]
pheatmap(cocl2tocis_pcnts_rand,cluster_rows = F, color = inferno(11), main = "CoCl2 to Cis Rand", breaks = breaks, cluster_cols = F)

# Cis 
cis_pcnts <- as.data.frame(sapply(cis_lin_clust_list, '[[', 3))
cell_nums <- sapply(cis_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cis_pcnts) <- c("0","1","2","3","4","5","6","7")
colnames(cis_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cis_pcnts,cluster_rows = F, color = inferno(10), main = "Cis", breaks = breaks)
p

cis_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cis_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cis_lin_clust_rand_list))
rownames(cis_pcnts_rand) <- c("0","1","2","3","4","5","6","7")
colnames(cis_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cis_pcnts_rand <- cis_pcnts_rand[,p$tree_col$order]
pheatmap(cis_pcnts_rand,cluster_rows = F, color = inferno(11), main = "Cis Rand", breaks = breaks, cluster_cols = F)

# Cis to DabTram
cistodabtram_pcnts <- as.data.frame(sapply(cistodabtram_lin_clust_list, '[[', 3))
cell_nums <- sapply(cistodabtram_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cistodabtram_pcnts) <- c("0","1","2","3","4","5","6","7")
colnames(cistodabtram_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cistodabtram_pcnts,cluster_rows = F, color = inferno(11), main = "Cis to DabTram", breaks = breaks)
p

cistodabtram_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cistodabtram_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cistodabtram_lin_clust_rand_list))
rownames(cistodabtram_pcnts_rand) <- c("0","1","2","3","4","5","6","7")
colnames(cistodabtram_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cistodabtram_pcnts_rand <- cistodabtram_pcnts_rand[,p$tree_col$order]
pheatmap(cistodabtram_pcnts_rand,cluster_rows = F, color = inferno(11), main = "Cis to DabTram Rand", breaks = breaks, cluster_cols = F)

# Cis to CoCl2
cistococl2_pcnts <- as.data.frame(sapply(cistococl2_lin_clust_list, '[[', 3))
cell_nums <- sapply(cistococl2_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cistococl2_pcnts) <- c("0","1","2","3","4","5","6")
colnames(cistococl2_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cistococl2_pcnts,cluster_rows = F, color = inferno(11), main = "Cis to CoCl2", breaks = breaks)
p

cistococl2_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cistococl2_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cistococl2_lin_clust_rand_list))
rownames(cistococl2_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(cistococl2_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cistococl2_pcnts_rand <- cistococl2_pcnts_rand[,p$tree_col$order]
pheatmap(cistococl2_pcnts_rand,cluster_rows = F, color = inferno(11), main = "Cis to CoCl2 Rand", breaks = breaks, cluster_cols = F)

# Cis to Cis
cistocis_pcnts <- as.data.frame(sapply(cistocis_lin_clust_list, '[[', 3))
cell_nums <- sapply(cistocis_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cistocis_pcnts) <- c("0","1","2","3","4","5","6","7","8")
colnames(cistocis_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cistocis_pcnts,cluster_rows = F, color = inferno(11), main = "Cis to Cis", breaks = breaks)
p

cistocis_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cistocis_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cistocis_lin_clust_rand_list))
rownames(cistocis_pcnts_rand) <- c("0","1","2","3","4","5","6","7","8")
colnames(cistocis_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cistocis_pcnts_rand <- cistocis_pcnts_rand[,p$tree_col$order]
pheatmap(cistocis_pcnts_rand,cluster_rows = F, color = inferno(11), main = "Cis to Cis Rand", breaks = breaks, cluster_cols = F)
dev.off()

```

# plot the different test statistics - histogram of weighted means of maximum contribution to lineage vs true
```{r}

pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/weighted_mean_cluster_assignments_test_stats.pdf')
# DabTram
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtram_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(dabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#623594', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'DabTram') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# DabTram to DabTram
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(dabtramtodabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#561e59', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'DabTram to DabTram') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# DabTram to CoCl2
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtococl2_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(dabtramtococl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#A2248E', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'DabTram to CoCl2') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# DabTram to Cis
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtocis_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(dabtramtocis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#9D85BE', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'DabTram to Cis') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# CoCl2
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cocl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#0F8241', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'CoCl2') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# CoCl2 to DabTram
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2todabtram_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cocl2todabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#10413B', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'CoCl2 to DabTram') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# CoCl2 to CoCl2
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tococl2_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cocl2tococl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#6ABD45', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'CoCl2 to CoCl2') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# CoCl2 to Cis
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tocis_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cocl2tocis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#6DC49C', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'CoCl2 to Cis') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# Cis
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cis_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#C96D29', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'Cis') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# Cis to DabTram
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cistodabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#A23622', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'Cis to DabTram') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# Cis to CoCl2
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistococl2_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cistococl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#C96D29', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'Cis to CoCl2') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# Cis to Cis
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistocis_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cistocis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#FBD08C', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'Cis to Cis') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
dev.off()

```



